09132020_PMRC
Jessica Han
7/8/2020
Load packages
Load and read data
setwd('/Users/giovanna/Box/431_Denu_microbiome_metabolic_health')
pmrc <- read.csv("/Users/giovanna/Box/431_Denu_microbiome_metabolic_health/dr431_pmrc_20200403_with_formats_for_documentation.csv", na.strings = c("[. ] Missing"))
warrior <- read.csv("/Users/giovanna/Box/431_Denu_microbiome_metabolic_health/dr431_warrior_20190911_with_formats_for_documentation.csv", na.strings = c("[. ] Missing"))
spid <- read.csv("/Users/giovanna/Box/431_Denu_microbiome_metabolic_health/dr431_warrior_pmrc_spid_crosswalk.csv")
seq_id <- read.csv("/Users/giovanna/OneDrive - UW-Madison/Rotations/3 Denu-Rey/SHOW data/meta_data.csv")
table1.df <- dplyr::inner_join(seq_id, spid, by="NEW_ID")
table2.df <- dplyr::inner_join(table1.df, warrior, by="NEW_ID")
table3.df <- merge(table2.df, pmrc, by = "NEW_ID")
# table3.df contains PMRC subjects with their information from warrior and pmrc.
names(table3.df)[names(table3.df) == "index"] <- "16S_ID"Create contingency table of gender, age, and seven indicatives for MetS
BMI (ANT_BMI); Triglycerides (PMRC_LAB_TRIG); HDL cholesterol (PMRC_LAB_HDLCHOL); Systolic BP (BP_SYSTOLIC_23); Diastolic BP (BP_DIASTOLIC_23); Glucose (PMRC_LAB_GLU), Hemoglobin 1ac (PMRC_LAB_GH); Waist Circumference (ANT_MEAS_WAIST_CM)
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.3 ✓ purrr 0.3.4
## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()#library(dplyr)
MetS_feature <- table3.df %>%
select("spid", "NEW_ID", "PMRC_ID","16S_ID", "ANT_BMI", "PMRC_LAB_TRIG","PMRC_LAB_HDLCHOL", "BP_SYSTOLIC_23", "BP_DIASTOLIC_23", "PMRC_LAB_GLU", "PMRC_LAB_GH", "GENDER", "AGE_CONSENT", "ANT_MEAS_WAIST_CM", "PMRC_FASTING_8HOURS")
mean(MetS_feature$AGE_CONSENT)## [1] 57.23264median(MetS_feature$AGE_CONSENT)## [1] 61A Subset: Fasting population
MetS_feature_fasting <- MetS_feature %>%
filter(PMRC_FASTING_8HOURS == "[1] Yes") %>%
select("spid","16S_ID", "ANT_BMI", "PMRC_LAB_TRIG","PMRC_LAB_HDLCHOL", "BP_SYSTOLIC_23", "BP_DIASTOLIC_23", "PMRC_LAB_GLU", "PMRC_LAB_GH", "GENDER", "AGE_CONSENT", "ANT_MEAS_WAIST_CM", "PMRC_FASTING_8HOURS") %>%
na.omit
nrow(MetS_feature_fasting)## [1] 81# Among total of 288 plasma subjects, 81 have fasted
MetS_feature_fasting_female <- MetS_feature_fasting %>%
filter(GENDER == "[2] Female") %>%
select("spid","16S_ID", "ANT_BMI", "PMRC_LAB_TRIG","PMRC_LAB_HDLCHOL", "BP_SYSTOLIC_23", "BP_DIASTOLIC_23", "PMRC_LAB_GLU", "PMRC_LAB_GH", "GENDER", "AGE_CONSENT", "ANT_MEAS_WAIST_CM", "PMRC_FASTING_8HOURS") %>%
na.omit
nrow(MetS_feature_fasting_female)## [1] 51# 51 out of 81 are fasted female
MetS_feature_fasting_male <- MetS_feature_fasting %>%
filter(GENDER == "[1] Male") %>%
select("spid","16S_ID", "ANT_BMI", "PMRC_LAB_TRIG","PMRC_LAB_HDLCHOL", "BP_SYSTOLIC_23", "BP_DIASTOLIC_23", "PMRC_LAB_GLU", "PMRC_LAB_GH", "GENDER", "AGE_CONSENT", "ANT_MEAS_WAIST_CM", "PMRC_FASTING_8HOURS") %>%
na.omit
nrow(MetS_feature_fasting_male)## [1] 30# 30 out of 81 are fasted maleDiagnose subjects using NCEP ATP III’s MetS definition (doi: 10.1242/dmm.001180)
Waist Circumference over 102cm (men) or 89cm (women); bp over 130/85mmHg, fasting TG over 150mg/dL, fasting HDL cholesterol less than 40mg/dl (men) or 50mg/dl (women); fasting blood sugar over 100mg/dl
#Female
MetS_feature_female <- MetS_feature %>% filter(GENDER == "[2] Female")
bmi.binary <- ifelse(MetS_feature_fasting_female$ANT_BMI>=30,1,0)
sysbp.binary <- ifelse(MetS_feature_fasting_female$BP_SYSTOLIC_23>=130,1,0)
diabp.binary <- ifelse(MetS_feature_fasting_female$BP_DIASTOLIC_23>=85,1,0)
tg.binary <- ifelse(MetS_feature_fasting_female$PMRC_LAB_TRIG>=150,1,0)
hdl.binary <- ifelse(MetS_feature_fasting_female$PMRC_LAB_HDLCHOL<50,1,0)
glucose.binary <- ifelse(MetS_feature$PMRC_LAB_GLU>=100,1,0)
#Male
MetS_feature_male <- MetS_feature %>% filter(GENDER == "[1] Male")
bmi.binary <- ifelse(MetS_feature_fasting_male$ANT_BMI>=30,1,0)
sysbp.binary <- ifelse(MetS_feature_fasting_male$BP_SYSTOLIC_23>=130,1,0)
diabp.binary <- ifelse(MetS_feature_fasting_male$BP_DIASTOLIC_23>=85,1,0)
tg.binary <- ifelse(MetS_feature_fasting_male$PMRC_LAB_TRIG>=150,1,0)
hdl.binary <- ifelse(MetS_feature_fasting_male$PMRC_LAB_HDLCHOL<40,1,0)
glucose.binary <- ifelse(MetS_feature$PMRC_LAB_GLU>=100,1,0)
#HbA1C from all plasma subjects (both fasting and non-fasting); 1 = unhealthy
gh.binary <- ifelse(MetS_feature$PMRC_LAB_GH>6.5,1,0)
#If three or more criteria meet, then assigned to 1; otherwise 0 | (1 = unhealthy)
variable <- bmi.binary + sysbp.binary + diabp.binary + tg.binary + hdl.binary + glucose.binary## Warning in bmi.binary + sysbp.binary + diabp.binary + tg.binary + hdl.binary + :
## longer object length is not a multiple of shorter object lengthMetS.binary <- ifelse(variable>=3, 1, 0)
#Append gh.binary and MetS.binary to MetS_feature table
MetS_feature <- cbind(MetS_feature, gh.binary, MetS.binary)
#Combine MetS.binary and gh.binary for MetS; 0 = healthy; 1 = diseased
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
#Number of diseased people in PMRC
diseased_PMRC <- MetS_feature %>%
filter(MetS == "1") %>%
select("spid", "MetS") %>%
na.omit
nrow(diseased_PMRC)## [1] 58#58 have metabolic disease out of 288 PMRC people.Correlation Test between age vs. phe
#male
MetS_feature_Male_num <- MetS_feature_male %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS) %>% na.omit
MetS_feature_Male_num <- as.data.frame(MetS_feature_Male_num)
cols <- t( combn(colnames(MetS_feature_Male_num),2) )
pvalue <- apply( cols , 1 , function(x) cor.test( MetS_feature_Male_num[,x[1] ] , MetS_feature_Male_num[ , x[2] ] )$p.value )
estimate <- apply( cols , 1 , function(x) cor.test( MetS_feature_Male_num[,x[1] ] , MetS_feature_Male_num[ , x[2] ] )$estimate )
cbind(cols,estimate,pvalue)## estimate
## [1,] "spid" "ANT_BMI" "0.0737676369000104"
## [2,] "spid" "PMRC_LAB_TRIG" "0.0703089070544183"
## [3,] "spid" "PMRC_LAB_HDLCHOL" "-0.343012730119083"
## [4,] "spid" "BP_SYSTOLIC_23" "0.30483218606412"
## [5,] "spid" "BP_DIASTOLIC_23" "0.0436452983236311"
## [6,] "spid" "PMRC_LAB_GLU" "0.0916168838459436"
## [7,] "spid" "PMRC_LAB_GH" "0.0994550047686194"
## [8,] "spid" "AGE_CONSENT" "0.240823211469777"
## [9,] "spid" "ANT_MEAS_WAIST_CM" "0.0923926802675844"
## [10,] "ANT_BMI" "PMRC_LAB_TRIG" "0.249677604887068"
## [11,] "ANT_BMI" "PMRC_LAB_HDLCHOL" "-0.445807827764368"
## [12,] "ANT_BMI" "BP_SYSTOLIC_23" "0.35016742845792"
## [13,] "ANT_BMI" "BP_DIASTOLIC_23" "0.14246947201599"
## [14,] "ANT_BMI" "PMRC_LAB_GLU" "0.317206607191021"
## [15,] "ANT_BMI" "PMRC_LAB_GH" "0.467863818003446"
## [16,] "ANT_BMI" "AGE_CONSENT" "0.140740854422792"
## [17,] "ANT_BMI" "ANT_MEAS_WAIST_CM" "0.925274822587552"
## [18,] "PMRC_LAB_TRIG" "PMRC_LAB_HDLCHOL" "-0.427998632659358"
## [19,] "PMRC_LAB_TRIG" "BP_SYSTOLIC_23" "0.144703464525829"
## [20,] "PMRC_LAB_TRIG" "BP_DIASTOLIC_23" "0.084533925156262"
## [21,] "PMRC_LAB_TRIG" "PMRC_LAB_GLU" "0.457035328239849"
## [22,] "PMRC_LAB_TRIG" "PMRC_LAB_GH" "0.581611448143058"
## [23,] "PMRC_LAB_TRIG" "AGE_CONSENT" "0.160145098779324"
## [24,] "PMRC_LAB_TRIG" "ANT_MEAS_WAIST_CM" "0.305380965191105"
## [25,] "PMRC_LAB_HDLCHOL" "BP_SYSTOLIC_23" "-0.187073692949041"
## [26,] "PMRC_LAB_HDLCHOL" "BP_DIASTOLIC_23" "0.00913657677625998"
## [27,] "PMRC_LAB_HDLCHOL" "PMRC_LAB_GLU" "-0.297070230218143"
## [28,] "PMRC_LAB_HDLCHOL" "PMRC_LAB_GH" "-0.268873976863689"
## [29,] "PMRC_LAB_HDLCHOL" "AGE_CONSENT" "-0.273463820596766"
## [30,] "PMRC_LAB_HDLCHOL" "ANT_MEAS_WAIST_CM" "-0.538359956384293"
## [31,] "BP_SYSTOLIC_23" "BP_DIASTOLIC_23" "0.509709133353408"
## [32,] "BP_SYSTOLIC_23" "PMRC_LAB_GLU" "0.0866752112578173"
## [33,] "BP_SYSTOLIC_23" "PMRC_LAB_GH" "0.150461327216623"
## [34,] "BP_SYSTOLIC_23" "AGE_CONSENT" "0.185717001029537"
## [35,] "BP_SYSTOLIC_23" "ANT_MEAS_WAIST_CM" "0.353448769342036"
## [36,] "BP_DIASTOLIC_23" "PMRC_LAB_GLU" "-0.0892847794384579"
## [37,] "BP_DIASTOLIC_23" "PMRC_LAB_GH" "-0.225970504929801"
## [38,] "BP_DIASTOLIC_23" "AGE_CONSENT" "-0.138191151450625"
## [39,] "BP_DIASTOLIC_23" "ANT_MEAS_WAIST_CM" "0.0937420587039849"
## [40,] "PMRC_LAB_GLU" "PMRC_LAB_GH" "0.659985090135718"
## [41,] "PMRC_LAB_GLU" "AGE_CONSENT" "0.287757875123229"
## [42,] "PMRC_LAB_GLU" "ANT_MEAS_WAIST_CM" "0.324563673059983"
## [43,] "PMRC_LAB_GH" "AGE_CONSENT" "0.408673601513299"
## [44,] "PMRC_LAB_GH" "ANT_MEAS_WAIST_CM" "0.476900738936989"
## [45,] "AGE_CONSENT" "ANT_MEAS_WAIST_CM" "0.35583883262147"
## pvalue
## [1,] "0.63827347643845"
## [2,] "0.654141792073578"
## [3,] "0.0243344546303565"
## [4,] "0.046855678358561"
## [5,] "0.781088934018117"
## [6,] "0.559018402993811"
## [7,] "0.525735811918712"
## [8,] "0.119792755506533"
## [9,] "0.555680762367655"
## [10,] "0.106377923474596"
## [11,] "0.00273468407392845"
## [12,] "0.0213376814367453"
## [13,] "0.362102260933719"
## [14,] "0.0382064488487508"
## [15,] "0.00155799624669168"
## [16,] "0.368015717770113"
## [17,] "7.29661291568305e-19"
## [18,] "0.00419574406269305"
## [19,] "0.354545436374883"
## [20,] "0.589915875477805"
## [21,] "0.00206318956412204"
## [22,] "4.30752574388176e-05"
## [23,] "0.304968536028839"
## [24,] "0.0464411217307064"
## [25,] "0.229667506628989"
## [26,] "0.953630541966013"
## [27,] "0.053046759539573"
## [28,] "0.081257525387583"
## [29,] "0.0760009714729006"
## [30,] "0.000196195713317478"
## [31,] "0.000480800947169436"
## [32,] "0.580495509259541"
## [33,] "0.335514997058498"
## [34,] "0.233124523814311"
## [35,] "0.0200705091089707"
## [36,] "0.569107647905036"
## [37,] "0.145103670914154"
## [38,] "0.376842976445718"
## [39,] "0.549897869129921"
## [40,] "1.47553589778814e-06"
## [41,] "0.0613249453850728"
## [42,] "0.0337160117576358"
## [43,] "0.00651200268786292"
## [44,] "0.00122376459816195"
## [45,] "0.0191878497108741"#female
MetS_feature_Female_num <- MetS_feature_female %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS) %>% na.omit
MetS_feature_Female_num <- as.data.frame(MetS_feature_Female_num)
cols <- t( combn(colnames(MetS_feature_Female_num),2) )
pvalue <- apply( cols , 1 , function(x) cor.test( MetS_feature_Female_num[,x[1] ] , MetS_feature_Female_num[ , x[2] ] )$p.value )
estimate <- apply( cols , 1 , function(x) cor.test( MetS_feature_Female_num[,x[1] ] , MetS_feature_Female_num[ , x[2] ] )$estimate )
cbind(cols,estimate,pvalue)## estimate
## [1,] "spid" "ANT_BMI" "-0.0490427583449091"
## [2,] "spid" "PMRC_LAB_TRIG" "-0.0944112129377198"
## [3,] "spid" "PMRC_LAB_HDLCHOL" "0.0622980644953981"
## [4,] "spid" "BP_SYSTOLIC_23" "0.0605914162138858"
## [5,] "spid" "BP_DIASTOLIC_23" "0.00328530803093169"
## [6,] "spid" "PMRC_LAB_GLU" "0.036438819737244"
## [7,] "spid" "PMRC_LAB_GH" "0.0912220453467413"
## [8,] "spid" "AGE_CONSENT" "0.0859660647544104"
## [9,] "spid" "ANT_MEAS_WAIST_CM" "-0.102715034915411"
## [10,] "ANT_BMI" "PMRC_LAB_TRIG" "0.190576307263433"
## [11,] "ANT_BMI" "PMRC_LAB_HDLCHOL" "-0.36861192884788"
## [12,] "ANT_BMI" "BP_SYSTOLIC_23" "0.0824274038762594"
## [13,] "ANT_BMI" "BP_DIASTOLIC_23" "0.103886621055278"
## [14,] "ANT_BMI" "PMRC_LAB_GLU" "0.337996569682703"
## [15,] "ANT_BMI" "PMRC_LAB_GH" "0.364506113359263"
## [16,] "ANT_BMI" "AGE_CONSENT" "0.0694134568915561"
## [17,] "ANT_BMI" "ANT_MEAS_WAIST_CM" "0.90596823426512"
## [18,] "PMRC_LAB_TRIG" "PMRC_LAB_HDLCHOL" "-0.362820208315054"
## [19,] "PMRC_LAB_TRIG" "BP_SYSTOLIC_23" "0.148148623830093"
## [20,] "PMRC_LAB_TRIG" "BP_DIASTOLIC_23" "0.277727865526065"
## [21,] "PMRC_LAB_TRIG" "PMRC_LAB_GLU" "0.176225697251854"
## [22,] "PMRC_LAB_TRIG" "PMRC_LAB_GH" "0.0524044882573218"
## [23,] "PMRC_LAB_TRIG" "AGE_CONSENT" "0.0542221007388887"
## [24,] "PMRC_LAB_TRIG" "ANT_MEAS_WAIST_CM" "0.229506685835264"
## [25,] "PMRC_LAB_HDLCHOL" "BP_SYSTOLIC_23" "0.0994153263448992"
## [26,] "PMRC_LAB_HDLCHOL" "BP_DIASTOLIC_23" "0.0567105534572735"
## [27,] "PMRC_LAB_HDLCHOL" "PMRC_LAB_GLU" "-0.234403515565394"
## [28,] "PMRC_LAB_HDLCHOL" "PMRC_LAB_GH" "-0.180901377298022"
## [29,] "PMRC_LAB_HDLCHOL" "AGE_CONSENT" "0.0460823648432636"
## [30,] "PMRC_LAB_HDLCHOL" "ANT_MEAS_WAIST_CM" "-0.416629383061389"
## [31,] "BP_SYSTOLIC_23" "BP_DIASTOLIC_23" "0.474545109863028"
## [32,] "BP_SYSTOLIC_23" "PMRC_LAB_GLU" "-0.0258379850835637"
## [33,] "BP_SYSTOLIC_23" "PMRC_LAB_GH" "0.0897996897838481"
## [34,] "BP_SYSTOLIC_23" "AGE_CONSENT" "0.408417683441886"
## [35,] "BP_SYSTOLIC_23" "ANT_MEAS_WAIST_CM" "0.05109681812577"
## [36,] "BP_DIASTOLIC_23" "PMRC_LAB_GLU" "-0.0484249715768851"
## [37,] "BP_DIASTOLIC_23" "PMRC_LAB_GH" "-0.0834552927790453"
## [38,] "BP_DIASTOLIC_23" "AGE_CONSENT" "-0.163183000497014"
## [39,] "BP_DIASTOLIC_23" "ANT_MEAS_WAIST_CM" "0.0720319856308937"
## [40,] "PMRC_LAB_GLU" "PMRC_LAB_GH" "0.844286527919386"
## [41,] "PMRC_LAB_GLU" "AGE_CONSENT" "0.0654955052171833"
## [42,] "PMRC_LAB_GLU" "ANT_MEAS_WAIST_CM" "0.438200856627636"
## [43,] "PMRC_LAB_GH" "AGE_CONSENT" "0.131646688272445"
## [44,] "PMRC_LAB_GH" "ANT_MEAS_WAIST_CM" "0.427389453483393"
## [45,] "AGE_CONSENT" "ANT_MEAS_WAIST_CM" "0.0449317554696872"
## pvalue
## [1,] "0.669818103369586"
## [2,] "0.410966529622175"
## [3,] "0.587925073968445"
## [4,] "0.598211801955012"
## [5,] "0.977226143231739"
## [6,] "0.751449734098696"
## [7,] "0.427019610727418"
## [8,] "0.454242559810132"
## [9,] "0.370852027188444"
## [10,] "0.0946613014052198"
## [11,] "0.000897960014677077"
## [12,] "0.473097688274793"
## [13,] "0.365390696377115"
## [14,] "0.00247388909084854"
## [15,] "0.00103472072524153"
## [16,] "0.545927098240874"
## [17,] "4.27182636039728e-30"
## [18,] "0.00109615059750879"
## [19,] "0.195511781459032"
## [20,] "0.0138216733535293"
## [21,] "0.122743208736308"
## [22,] "0.648630977388502"
## [23,] "0.637289273704681"
## [24,] "0.0432497533193096"
## [25,] "0.386498471975937"
## [26,] "0.621896076027088"
## [27,] "0.0388618869934056"
## [28,] "0.112968525050499"
## [29,] "0.688692179737485"
## [30,] "0.00014811099138146"
## [31,] "1.13695073524638e-05"
## [32,] "0.822330811858397"
## [33,] "0.434293108478106"
## [34,] "0.000205567035491337"
## [35,] "0.65684058363499"
## [36,] "0.673740464938856"
## [37,] "0.467577682205579"
## [38,] "0.153431997826268"
## [39,] "0.530847275543755"
## [40,] "2.76231525177494e-22"
## [41,] "0.568871750350499"
## [42,] "6.00776225052311e-05"
## [43,] "0.250593459267857"
## [44,] "9.51517427989896e-05"
## [45,] "0.696080287728924"Visualization - Scatter plot: Features vs Age by Gender; Using whole PMRC
#Divide the age into quantiles
agegroup_quantile <- quantile(MetS_feature$AGE_CONSENT, probs = seq(0,1,0.3333333), na.rm = TRUE, names = TRUE, type = 1)
agegroup_quantile## 0% 33.33333% 66.66666% 99.99999%
## 18 53 66 88#age between 18-53: Young; 54-66: middle; 67-88: old
##Divide the agegroup by quantile
MetS_feature <- MetS_feature %>% mutate(ageGroup=case_when(
AGE_CONSENT<=agegroup_quantile[2] ~ "young",
AGE_CONSENT<=agegroup_quantile[3] ~ "middle",
AGE_CONSENT<=agegroup_quantile[4] ~ "old"
))
##Plot the agegroup vs. phe
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=ANT_BMI, color=ageGroup))+
geom_point()## Warning: Removed 3 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=PMRC_LAB_TRIG, color=ageGroup))+
geom_point()## Warning: Removed 161 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=PMRC_LAB_HDLCHOL, color=ageGroup))+
geom_point()## Warning: Removed 164 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=BP_SYSTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=BP_DIASTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=PMRC_LAB_GLU, color=ageGroup))+
geom_point()## Warning: Removed 164 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=PMRC_LAB_GH, color=ageGroup))+
geom_point()## Warning: Removed 164 rows containing missing values (geom_point).
ggplot(MetS_feature,aes(x=AGE_CONSENT, y=ANT_MEAS_WAIST_CM, color=ageGroup))+
geom_point()
#PMRC_female
#Divide the age into quantiles
#female
agegroup_quantile <- quantile(MetS_feature_female$AGE_CONSENT, probs = seq(0,1,0.3333333), na.rm = TRUE, names = TRUE, type = 1)
agegroup_quantile## 0% 33.33333% 66.66666% 99.99999%
## 22 52 64 85#age between 22-52: Young female; 53-64: middle female; 65-85: old female
##Assign the ageGroup for female
MetS_feature_female <- MetS_feature_female %>% mutate(ageGroup=case_when(
AGE_CONSENT<=agegroup_quantile[2] ~ "young",
AGE_CONSENT<=agegroup_quantile[3] ~ "middle",
AGE_CONSENT<=agegroup_quantile[4] ~ "old"
))
#Plot the agegroup vs. phe for female
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=ANT_BMI, color=ageGroup))+
geom_point()## Warning: Removed 2 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=PMRC_LAB_TRIG, color=ageGroup))+
geom_point()## Warning: Removed 88 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=PMRC_LAB_HDLCHOL, color=ageGroup))+
geom_point()## Warning: Removed 90 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=BP_SYSTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=BP_DIASTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=PMRC_LAB_GLU, color=ageGroup))+
geom_point()## Warning: Removed 90 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=PMRC_LAB_GH, color=ageGroup))+
geom_point()## Warning: Removed 90 rows containing missing values (geom_point).
ggplot(MetS_feature_female,aes(x=AGE_CONSENT, y=ANT_MEAS_WAIST_CM, color=ageGroup))+
geom_point()
#Correlation Test by ageGroup
#ageGroup==young
MetS_feature_Female_ageGroup_young <- MetS_feature_female %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Female_ageGroup_young <- MetS_feature_female %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Female_ageGroup_young <- as.data.frame(MetS_feature_Female_ageGroup_young)
res.bmi <- cor.test(MetS_feature_Female_ageGroup_young$AGE_CONSENT, MetS_feature_Female_ageGroup_young$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Female_ageGroup_young$PMRC_LAB_TRIG, MetS_feature_Female_ageGroup_young$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Female_ageGroup_young$PMRC_LAB_HDLCHOL, MetS_feature_Female_ageGroup_young$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Female_ageGroup_young$BP_SYSTOLIC_23, MetS_feature_Female_ageGroup_young$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Female_ageGroup_young$BP_DIASTOLIC_23, MetS_feature_Female_ageGroup_young$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Female_ageGroup_young$PMRC_LAB_GLU, MetS_feature_Female_ageGroup_young$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Female_ageGroup_young$PMRC_LAB_GH, MetS_feature_Female_ageGroup_young$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Female_ageGroup_young$ANT_MEAS_WAIST_CM, MetS_feature_Female_ageGroup_young$ANT_MEAS_WAIST_CM,
method = "pearson")
#ageGroup==middle
MetS_feature_Female_ageGroup_middle <- MetS_feature_female %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Female_ageGroup_middle <- MetS_feature_female %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Female_ageGroup_middle <- as.data.frame(MetS_feature_Female_ageGroup_middle)
res.bmi <- cor.test(MetS_feature_Female_ageGroup_middle$AGE_CONSENT, MetS_feature_Female_ageGroup_middle$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Female_ageGroup_middle$PMRC_LAB_TRIG, MetS_feature_Female_ageGroup_middle$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Female_ageGroup_middle$PMRC_LAB_HDLCHOL, MetS_feature_Female_ageGroup_middle$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Female_ageGroup_middle$BP_SYSTOLIC_23, MetS_feature_Female_ageGroup_middle$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Female_ageGroup_middle$BP_DIASTOLIC_23, MetS_feature_Female_ageGroup_middle$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Female_ageGroup_middle$PMRC_LAB_GLU, MetS_feature_Female_ageGroup_middle$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Female_ageGroup_middle$PMRC_LAB_GH, MetS_feature_Female_ageGroup_middle$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Female_ageGroup_middle$ANT_MEAS_WAIST_CM, MetS_feature_Female_ageGroup_middle$ANT_MEAS_WAIST_CM,
method = "pearson")
#ageGroup==old
MetS_feature_Female_ageGroup_old <- MetS_feature_female %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Female_ageGroup_old <- MetS_feature_female %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Female_ageGroup_old <- as.data.frame(MetS_feature_Female_ageGroup_old)
res.bmi <- cor.test(MetS_feature_Female_ageGroup_old$AGE_CONSENT, MetS_feature_Female_ageGroup_old$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Female_ageGroup_old$PMRC_LAB_TRIG, MetS_feature_Female_ageGroup_old$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Female_ageGroup_old$PMRC_LAB_HDLCHOL, MetS_feature_Female_ageGroup_old$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Female_ageGroup_old$BP_SYSTOLIC_23, MetS_feature_Female_ageGroup_old$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Female_ageGroup_old$BP_DIASTOLIC_23, MetS_feature_Female_ageGroup_old$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Female_ageGroup_old$PMRC_LAB_GLU, MetS_feature_Female_ageGroup_old$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Female_ageGroup_old$PMRC_LAB_GH, MetS_feature_Female_ageGroup_old$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Female_ageGroup_old$ANT_MEAS_WAIST_CM, MetS_feature_Female_ageGroup_old$ANT_MEAS_WAIST_CM,
method = "pearson")#PMRC_male
#male
agegroup_quantile <- quantile(MetS_feature_male$AGE_CONSENT, probs = seq(0,1,0.3333333), na.rm = TRUE, names = TRUE, type = 1)
agegroup_quantile## 0% 33.33333% 66.66666% 99.99999%
## 18 53 68 88#age between 26-55: Young male; 56-66: middle male; 67-82: old male
##Assign the ageGroup for male
MetS_feature_male <- MetS_feature_male %>% mutate(ageGroup=case_when(
AGE_CONSENT<=agegroup_quantile[2] ~ "young",
AGE_CONSENT<=agegroup_quantile[3] ~ "middle",
AGE_CONSENT<=agegroup_quantile[4] ~ "old"
))
#Plot the agegroup vs. phe for male
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=ANT_BMI, color=ageGroup))+
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=PMRC_LAB_TRIG, color=ageGroup))+
geom_point()## Warning: Removed 73 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=PMRC_LAB_HDLCHOL, color=ageGroup))+
geom_point()## Warning: Removed 74 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=BP_SYSTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=BP_DIASTOLIC_23, color=ageGroup))+
geom_point()## Warning: Removed 1 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=PMRC_LAB_GLU, color=ageGroup))+
geom_point()## Warning: Removed 74 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=PMRC_LAB_GH, color=ageGroup))+
geom_point()## Warning: Removed 74 rows containing missing values (geom_point).
ggplot(MetS_feature_male,aes(x=AGE_CONSENT, y=ANT_MEAS_WAIST_CM, color=ageGroup))+
geom_point()
#Correlation Test by ageGroup
#ageGroup==young
MetS_feature_Male_ageGroup_young <- MetS_feature_male %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Male_ageGroup_young <- MetS_feature_male %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Male_ageGroup_young <- as.data.frame(MetS_feature_Male_ageGroup_young)
res.bmi <- cor.test(MetS_feature_Male_ageGroup_young$AGE_CONSENT, MetS_feature_Male_ageGroup_young$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Male_ageGroup_young$PMRC_LAB_TRIG, MetS_feature_Male_ageGroup_young$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Male_ageGroup_young$PMRC_LAB_HDLCHOL, MetS_feature_Male_ageGroup_young$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Male_ageGroup_young$BP_SYSTOLIC_23, MetS_feature_Male_ageGroup_young$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Male_ageGroup_young$BP_DIASTOLIC_23, MetS_feature_Male_ageGroup_young$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Male_ageGroup_young$PMRC_LAB_GLU, MetS_feature_Male_ageGroup_young$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Male_ageGroup_young$PMRC_LAB_GH, MetS_feature_Male_ageGroup_young$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Male_ageGroup_young$ANT_MEAS_WAIST_CM, MetS_feature_Male_ageGroup_young$ANT_MEAS_WAIST_CM,
method = "pearson")
#ageGroup==middle
MetS_feature_Male_ageGroup_middle <- MetS_feature_male %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Male_ageGroup_middle <- MetS_feature_male %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Male_ageGroup_middle <- as.data.frame(MetS_feature_Male_ageGroup_middle)
res.bmi <- cor.test(MetS_feature_Male_ageGroup_middle$AGE_CONSENT, MetS_feature_Male_ageGroup_middle$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Male_ageGroup_middle$PMRC_LAB_TRIG, MetS_feature_Male_ageGroup_middle$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Male_ageGroup_middle$PMRC_LAB_HDLCHOL, MetS_feature_Male_ageGroup_middle$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Male_ageGroup_middle$BP_SYSTOLIC_23, MetS_feature_Male_ageGroup_middle$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Male_ageGroup_middle$BP_DIASTOLIC_23, MetS_feature_Male_ageGroup_middle$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Male_ageGroup_middle$PMRC_LAB_GLU, MetS_feature_Male_ageGroup_middle$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Male_ageGroup_middle$PMRC_LAB_GH, MetS_feature_Male_ageGroup_middle$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Male_ageGroup_middle$ANT_MEAS_WAIST_CM, MetS_feature_Male_ageGroup_middle$ANT_MEAS_WAIST_CM,
method = "pearson")
#ageGroup==old
MetS_feature_Male_ageGroup_old <- MetS_feature_male %>% select(-NEW_ID, -PMRC_ID, -`16S_ID`, -GENDER, -PMRC_FASTING_8HOURS, -ageGroup) %>% na.omit
MetS_feature_Male_ageGroup_old <- MetS_feature_male %>% filter(ageGroup=="young") %>% na.omit
MetS_feature_Male_ageGroup_old <- as.data.frame(MetS_feature_Male_ageGroup_old)
res.bmi <- cor.test(MetS_feature_Male_ageGroup_old$AGE_CONSENT, MetS_feature_Male_ageGroup_old$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_Male_ageGroup_old$PMRC_LAB_TRIG, MetS_feature_Male_ageGroup_old$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_Male_ageGroup_old$PMRC_LAB_HDLCHOL, MetS_feature_Male_ageGroup_old$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_Male_ageGroup_old$BP_SYSTOLIC_23, MetS_feature_Male_ageGroup_old$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_Male_ageGroup_old$BP_DIASTOLIC_23, MetS_feature_Male_ageGroup_old$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_Male_ageGroup_old$PMRC_LAB_GLU, MetS_feature_Male_ageGroup_old$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_Male_ageGroup_old$PMRC_LAB_GH, MetS_feature_Male_ageGroup_old$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_Male_ageGroup_old$ANT_MEAS_WAIST_CM, MetS_feature_Male_ageGroup_old$ANT_MEAS_WAIST_CM,
method = "pearson")#Multicorr test with taxa # Correlation test between each phenotype and age in male
res.bmi <- cor.test(MetS_feature_male$AGE_CONSENT, MetS_feature_male$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_male$PMRC_LAB_TRIG, MetS_feature_male$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_male$PMRC_LAB_HDLCHOL, MetS_feature_male$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_male$BP_SYSTOLIC_23, MetS_feature_male$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_male$BP_DIASTOLIC_23, MetS_feature_male$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_male$PMRC_LAB_GLU, MetS_feature_male$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_male$PMRC_LAB_GH, MetS_feature_male$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_male$ANT_MEAS_WAIST_CM, MetS_feature_male$ANT_MEAS_WAIST_CM,
method = "pearson")Correlation test between each phenotype and age in female
res.bmi <- cor.test(MetS_feature_female$AGE_CONSENT, MetS_feature_female$ANT_BMI,
method = "pearson")
res.trig <- cor.test(MetS_feature_female$PMRC_LAB_TRIG, MetS_feature_female$PMRC_LAB_TRIG,
method = "pearson")
res.hdl <- cor.test(MetS_feature_female$PMRC_LAB_HDLCHOL, MetS_feature_female$PMRC_LAB_HDLCHOL,
method = "pearson")
res.sys <- cor.test(MetS_feature_female$BP_SYSTOLIC_23, MetS_feature_female$BP_SYSTOLIC_23,
method = "pearson")
res.dias <- cor.test(MetS_feature_female$BP_DIASTOLIC_23, MetS_feature_female$BP_DIASTOLIC_23,
method = "pearson")
res.glu <- cor.test(MetS_feature_female$PMRC_LAB_GLU, MetS_feature_female$PMRC_LAB_GLU,
method = "pearson")
res.gh <- cor.test(MetS_feature_female$PMRC_LAB_GH, MetS_feature_female$PMRC_LAB_GH,
method = "pearson")
res.waist <- cor.test(MetS_feature_female$ANT_MEAS_WAIST_CM, MetS_feature_female$ANT_MEAS_WAIST_CM,
method = "pearson")#Read dataframes_level2_Phylums
#ASVs <- read_qza("microbiome_data/filtered/table_filt.qza") #Gives me an error so I commented out.
taxa_table <- read.csv("level-2_phylum_taxtable_clean.csv", header=T)
taxa_table = taxa_table[,1:13] #all rows, from columns 1 to 13
taxa_ra <- sweep(taxa_table[,2:ncol(taxa_table)],1,rowSums(taxa_table[,2:ncol(taxa_table)]),"/") #relative abundance - normalization of sample reads
taxa_ra$index = taxa_table$index
merged.table_phylum <- merge(MetS_feature, taxa_ra, by.x="16S_ID", by.y="index")
merged.table_male <- merged.table_phylum %>%
filter(GENDER == "[1] Male")
merged.table_female <- merged.table_phylum %>%
filter(GENDER == "[2] Female")
#Regression
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
reg_data_phylum <- merged.table_phylum %>% select(MetS,AGE_CONSENT, ANT_BMI, PMRC_LAB_TRIG, PMRC_LAB_HDLCHOL, BP_SYSTOLIC_23, BP_DIASTOLIC_23, PMRC_LAB_GLU, PMRC_LAB_GH, ANT_MEAS_WAIST_CM, starts_with("D_0__"))
lmod <- glm(MetS ~ ., family = "binomial", data = reg_data_phylum)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(lmod)##
## Call:
## glm(formula = MetS ~ ., family = "binomial", data = reg_data_phylum)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.7910 -0.9004 -0.1359 0.7894 2.1031
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.662e+01 1.619e+01 1.026 0.3048
## AGE_CONSENT -3.450e-02 2.084e-02 -1.656 0.0978 .
## ANT_BMI 2.894e-02 7.601e-02 0.381 0.7034
## PMRC_LAB_TRIG 3.692e-03 3.423e-03 1.079 0.2808
## PMRC_LAB_HDLCHOL -1.821e-02 1.834e-02 -0.993 0.3205
## BP_SYSTOLIC_23 -1.095e-02 1.774e-02 -0.617 0.5370
## BP_DIASTOLIC_23 4.567e-03 3.210e-02 0.142 0.8869
## PMRC_LAB_GLU 2.466e-02 1.421e-02 1.735 0.0827 .
## PMRC_LAB_GH 1.311e+00 5.469e-01 2.396 0.0166 *
## ANT_MEAS_WAIST_CM -5.217e-02 4.020e-02 -1.298 0.1943
## D_0__Archaea.D_1__Euryarchaeota 4.495e+01 3.119e+01 1.441 0.1495
## D_0__Bacteria.D_1__Actinobacteria -6.181e+01 2.651e+01 -2.331 0.0197 *
## D_0__Bacteria.D_1__Bacteroidetes -1.716e+01 1.488e+01 -1.153 0.2489
## D_0__Bacteria.D_1__Cyanobacteria 8.583e+01 3.761e+02 0.228 0.8195
## D_0__Bacteria.D_1__Epsilonbacteraeota 5.610e+02 3.457e+03 0.162 0.8711
## D_0__Bacteria.D_1__Firmicutes -1.918e+01 1.503e+01 -1.276 0.2018
## D_0__Bacteria.D_1__Fusobacteria 3.572e+02 2.584e+02 1.382 0.1669
## D_0__Bacteria.D_1__Lentisphaerae -7.244e+04 5.748e+06 -0.013 0.9899
## D_0__Bacteria.D_1__Proteobacteria -2.566e+01 1.698e+01 -1.511 0.1307
## D_0__Bacteria.D_1__Synergistetes 3.226e+01 2.247e+02 0.144 0.8858
## D_0__Bacteria.D_1__Tenericutes -8.927e+01 7.894e+01 -1.131 0.2581
## D_0__Bacteria.D_1__Verrucomicrobia NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.07 on 120 degrees of freedom
## Residual deviance: 116.95 on 100 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 158.95
##
## Number of Fisher Scoring iterations: 16#Male
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_male <- colSums(Filter(is.numeric,merged.table_male))
zero_cols <- names(colsums_male[colsums_male==0 & !is.na(colsums_male)])
# re-define merged.tale without cols that sum to zero
merged.table_male <- merged.table_male %>% select(-one_of(zero_cols))
#Female
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_female <- colSums(Filter(is.numeric,merged.table_female))
zero_cols <- names(colsums_female[colsums_female==0 & !is.na(colsums_female)])
# re-define merged.tale without cols that sum to zero
merged.table_female <- merged.table_female %>% select(-one_of(zero_cols))#Correlation test
multi_assoc <- function(mdf,exp_vars, res_vars, p.zeros=.25,adj.zero=TRUE,method, inc.zeros=TRUE, run_ratio = TRUE){
#Arguments:
# mdf - dataframe where the explanitory and response variables are as columns and subjects are rows
# exp_vars - a vector of numbers that indicate the explanitory variable cols in mdf
# res_vars - a vector of numbers that indicate the response variable cols in mdf
# p.zeros - the max amount of zeros in a singe variable that will be tolerated (expressed as a percent)
# this taxa has to be 25% present in all samples to run the analysis.
# adj.zeros - add a small value to each datapoint (0.1% of the variable average) to allow for divide by zero
# add small number, so it's close to 0 but has some values (if running correlation with 0, causes problems)
# method - 'spearman', 'pearson'
# spearman brings values high to low [ranking]; whereas pearson uses the actual values; spearman corrects/reduces variation but p value less significant.
# inc.zeros - TRUE = include all zeros in exp_vars for analysis. FALSE = replace all zeros wtih NA
# run_ration - TRUE = it will run correlations for every exp_var1:exp_var2 combination to the response variable. If not "TRUE", will only run correlations with exp_var1 vs res_var
# modification of multi_assoc(method = 'spearman', ). Only divides and multiplys explanitory variables
if (inc.zeros==FALSE){
mdf[,exp_vars] <- apply(mdf[,exp_vars], 2, function(x) ifelse(x == 0, NA, x))
}
mdf$None <- c(rep(1,nrow(mdf)))
exp_vars <- c(exp_vars,match('None', colnames(mdf)))
d <- list()
n <- 0
t=1
col_headers <- c("Explanitory_var1","Explanitory_var2","Response_var", "Estimate", "c.Pval", "Rsq", 'r.Pval')
if(adj.zero == TRUE){ #check to see if adjustment was specified
adj=1
}else{
adj=0
}
for (i in exp_vars[-length(exp_vars)]) { #for every exp variable. This excludes the "None" column added in line above
if ((length(which(mdf[,i]==0|is.na(mdf[,i])==TRUE))<p.zeros*length(mdf[,i])) & (is.numeric(mdf[,i])==TRUE)){ # if the percent of zeros in exp var is less than the specified p.zero threshold, continue, else skip exp var
if (run_ratio == TRUE){
list2 <- exp_vars[-match(i, exp_vars)] #makes a list of all variables except for "i" variable to loop over (for divide only, see multiply below)
} else {
list2 <- 'None'
}
for (j in list2){
if ((length(which(mdf[,j]==0|is.na(mdf[,j])==TRUE))<p.zeros*length(mdf[,j])) & (is.numeric(mdf[,j])==TRUE)){ #check to see if comparison exp var meets p.zero threshold
for (x in res_vars){ #loops over every response variable
#run tests
z <- (mdf[,i]+adj*(0.001*mean(mdf[,i],na.rm = T)))/(mdf[,j]+adj*(0.001*mean(mdf[,j],na.rm = T)))
z[is.infinite(z)] <- NA
y <- mdf[,x]
tmp <- cor.test(y, z, method = method)
reg <- summary(lm(y~z))
tmp_df <- data.frame(
colnames(mdf[i]),
colnames(mdf[j]),
colnames(mdf[x]),
as.numeric(tmp[4]),
as.numeric(tmp[3]),
as.numeric(reg$r.squared),
as.numeric(try(pf(reg$fstatistic[1],reg$fstatistic[2],reg$fstatistic[3], lower.tail = F),silent = T)),
stringsAsFactors = FALSE)
n <- n+1
d[[n]] <- tmp_df
}
}
}
}
if (t > (length(exp_vars)/100)){ # prints status as a percentage
print(paste(match(i,exp_vars)/length(exp_vars)*100,"%"))
t=1
}else{
t=t+1
}
}
tmp_results <- do.call(rbind,d)
colnames(tmp_results) <- col_headers
tmp_results <- as_data_frame(tmp_results)
tmp_results$c.fdr <- p.adjust(tmp_results$c.Pval)
cp <- match("c.Pval", colnames(tmp_results))
tmp_results <- tmp_results[,c(1:cp, ncol(tmp_results),(cp+1):(ncol(tmp_results)-1))]
tmp_results$r.fdr <- p.adjust(tmp_results$r.Pval)
multi_corr_tbl <- tmp_results[order(tmp_results$c.Pval, decreasing = F),]
rownames(multi_corr_tbl) <- c(1:nrow(multi_corr_tbl))
multi_corr_tbl$index <- c(1:nrow(multi_corr_tbl))
if (run_ratio != TRUE){
multi_corr_tbl <- multi_corr_tbl[,-2]
}
return(multi_corr_tbl)
}
#####################################
#####################################
plot_comprsn <- function (data_tbl,comp_tbl,row,color.by='All samples'){
#Arguments:
# data_tbl - dataframe where the explanitory and response variables are as columns and subjects are rows (same df used in multi_pred_cor())
# comp_tbl - comparison table, this is the output of multi_pred_cor()
# row - the row of the comp_tbl you want to plot (row 1 is the most significant correlation)
# color.by - the col of data_table to use as input for aes(color =). Must specify the col with $.
if (colnames(comp_tbl[2]) != "Explanitory_var2"){
tops <- c(comp_tbl[[row,1]], "", comp_tbl[[row,2]])
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
tops <- c(comp_tbl[[row,1]], comp_tbl[[row,2]], comp_tbl[[row,3]])
if (tops[2]=="None"){
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
z <- do.call('/',list(data_tbl[[tops[1]]],data_tbl[[tops[2]]]))
z[is.infinite(z)] <- NA
label <- paste(tops[1], tops[2], sep = paste("_##","/","##_"))
}
}
y <- tops[3]
data_tbl$zn <- z
x <- 'zn'
my.formula <- x~y
if (color.by != "All samples"){
color.by <- data_tbl[,color.by]
colors <- scale_color_brewer(palette="Set1")
} else {
colors <- scale_color_manual(values = 'black')
}
plot <- data_tbl %>% ggplot(aes(x = data_tbl$zn, y = data_tbl[,y], color=color.by))+
xlab(label)+
ylab(tops[3])+
geom_point()+
colors+
stat_cor(method = 'spearman', label.y.npc = .95)+
geom_smooth(method=lm, se=F)
return(plot)
}#Scaterplot and heatmap ##BMI
#Try each phenotype of the disease: phe = c(6:12,14,15)
phe = c(5) #BMI
#phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"
## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"
## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: `as_data_frame()` is deprecated as of tibble 2.0.0.
## Please use `as_tibble()` instead.
## The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"
## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 2 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
##TRIG
phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 73 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing non-finite values (stat_smooth).## Warning: Removed 73 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 88 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 88 rows containing non-finite values (stat_smooth).## Warning: Removed 88 rows containing missing values (geom_point).
##HDL
phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##Systolic
phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##Diastolic
phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##GLU
phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##GH
phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##AGE
phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
##Waist
phe = c(14) #Waist
taxa = c(20:31) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:31) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.3846153846154 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.0769230769231 %"
## [1] "30.7692307692308 %"
## [1] "38.4615384615385 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "53.8461538461538 %"
## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"
## [1] "76.9230769230769 %"
## [1] "84.6153846153846 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
#Heatmap_level2
#Male
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)## corrplot 0.84 loadedres = cor.mtest(merged.table_male[,c(5:11,13,14,20:31)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_male[,c(5:11,13,14,20:31)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_male[, c(5:11, 13, 14, 20:31)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Female
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_female[,c(5:11,13,14,20:31)], conf.level = 0.95, rm.na = T)
Correlation <- round(cor(merged.table_female[,c(5:11,13,14,20:31)],method = "spearman", use="complete.obs"), 2)
corrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Read dataframes_level3_class
#ASVs <- read_qza("microbiome_data/filtered/table_filt.qza") #Gives me an error so I commented out.
taxa_table <- read.csv("level-3_class_taxtable_clean.csv", header=T)
taxa_table = taxa_table[,1:21] #all rows, from columns 1 to 21
taxa_ra <- sweep(taxa_table[,2:ncol(taxa_table)],1,rowSums(taxa_table[,2:ncol(taxa_table)]),"/") #relative abundance - normalization of sample reads
taxa_ra$index = taxa_table$index
merged.table_class <- merge(MetS_feature, taxa_ra, by.x="16S_ID", by.y="index")
merged.table_male <- merged.table_class %>%
filter(GENDER == "[1] Male")
merged.table_female <- merged.table_class %>%
filter(GENDER == "[2] Female")
#Regression
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
reg_data_class <- merged.table_class %>% select(MetS,AGE_CONSENT, ANT_BMI, PMRC_LAB_TRIG, PMRC_LAB_HDLCHOL, BP_SYSTOLIC_23, BP_DIASTOLIC_23, PMRC_LAB_GLU, PMRC_LAB_GH, ANT_MEAS_WAIST_CM, starts_with("D_0__"))
lmod <- glm(MetS ~ ., family = "binomial", data = reg_data_class)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(lmod)##
## Call:
## glm(formula = MetS ~ ., family = "binomial", data = reg_data_class)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.32446 -0.71129 -0.02904 0.59642 2.04943
##
## Coefficients: (1 not defined because of singularities)
## Estimate
## (Intercept) 2.254e+01
## AGE_CONSENT -8.165e-02
## ANT_BMI 2.063e-02
## PMRC_LAB_TRIG 5.487e-03
## PMRC_LAB_HDLCHOL -2.757e-02
## BP_SYSTOLIC_23 1.766e-02
## BP_DIASTOLIC_23 -2.825e-02
## PMRC_LAB_GLU 3.255e-02
## PMRC_LAB_GH 1.203e+00
## ANT_MEAS_WAIST_CM -6.906e-02
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria 2.969e+01
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata 1.035e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria -9.173e+01
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia 2.499e+01
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia -1.892e+01
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria 1.254e+03
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria -2.058e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli 1.228e+00
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia -2.337e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia -3.235e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes 8.779e+00
## D_0__Bacteria.D_1__Firmicutes.__ 9.883e+03
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia 4.699e+02
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria -7.689e+04
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria -8.053e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria -1.018e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria -3.090e+01
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia 4.632e+01
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes -3.169e+02
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae NA
## Std. Error z value
## (Intercept) 2.033e+01 1.109
## AGE_CONSENT 3.040e-02 -2.686
## ANT_BMI 1.024e-01 0.201
## PMRC_LAB_TRIG 3.919e-03 1.400
## PMRC_LAB_HDLCHOL 2.186e-02 -1.261
## BP_SYSTOLIC_23 2.361e-02 0.748
## BP_DIASTOLIC_23 3.844e-02 -0.735
## PMRC_LAB_GLU 1.687e-02 1.930
## PMRC_LAB_GH 7.625e-01 1.577
## ANT_MEAS_WAIST_CM 5.163e-02 -1.338
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria 3.970e+01 0.748
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata 4.566e+03 0.227
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria 3.618e+01 -2.535
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia 1.018e+02 0.246
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia 1.899e+01 -0.997
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria 7.061e+02 1.776
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria 5.597e+03 -0.037
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli 2.678e+01 0.046
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia 1.910e+01 -1.223
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia 2.791e+01 -1.159
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes 2.607e+01 0.337
## D_0__Bacteria.D_1__Firmicutes.__ 4.794e+03 2.061
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia 2.772e+02 1.695
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria 8.207e+06 -0.009
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria 4.681e+02 -1.720
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria 1.192e+02 -0.854
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria 2.053e+01 -1.505
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia 6.630e+02 0.070
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes 3.113e+02 -1.018
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae NA NA
## Pr(>|z|)
## (Intercept) 0.26746
## AGE_CONSENT 0.00724 **
## ANT_BMI 0.84038
## PMRC_LAB_TRIG 0.16148
## PMRC_LAB_HDLCHOL 0.20729
## BP_SYSTOLIC_23 0.45467
## BP_DIASTOLIC_23 0.46238
## PMRC_LAB_GLU 0.05362 .
## PMRC_LAB_GH 0.11469
## ANT_MEAS_WAIST_CM 0.18100
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria 0.45453
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata 0.82062
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria 0.01124 *
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia 0.80601
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia 0.31897
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria 0.07566 .
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria 0.97067
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli 0.96343
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia 0.22126
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia 0.24649
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes 0.73634
## D_0__Bacteria.D_1__Firmicutes.__ 0.03927 *
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia 0.09004 .
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria 0.99252
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria 0.08535 .
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria 0.39298
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria 0.13227
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia 0.94430
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes 0.30870
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.072 on 120 degrees of freedom
## Residual deviance: 94.889 on 92 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 152.89
##
## Number of Fisher Scoring iterations: 17#Male
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_male <- colSums(Filter(is.numeric,merged.table_male))
zero_cols <- names(colsums_male[colsums_male==0 & !is.na(colsums_male)])
# re-define merged.tale without cols that sum to zero
merged.table_male <- merged.table_male %>% select(-one_of(zero_cols))
#Female
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_female <- colSums(Filter(is.numeric,merged.table_female))
zero_cols <- names(colsums_female[colsums_female==0 & !is.na(colsums_female)])
# re-define merged.tale without cols that sum to zero
merged.table_female <- merged.table_female %>% select(-one_of(zero_cols))#Correlation test
multi_assoc <- function(mdf,exp_vars, res_vars, p.zeros=.25,adj.zero=TRUE,method, inc.zeros=TRUE, run_ratio = TRUE){
#Arguments:
# mdf - dataframe where the explanitory and response variables are as columns and subjects are rows
# exp_vars - a vector of numbers that indicate the explanitory variable cols in mdf
# res_vars - a vector of numbers that indicate the response variable cols in mdf
# p.zeros - the max amount of zeros in a singe variable that will be tolerated (expressed as a percent)
# this taxa has to be 25% present in all samples to run the analysis.
# adj.zeros - add a small value to each datapoint (0.1% of the variable average) to allow for divide by zero
# add small number, so it's close to 0 but has some values (if running correlation with 0, causes problems)
# method - 'spearman', 'pearson'
# spearman brings values high to low [ranking]; whereas pearson uses the actual values; spearman corrects/reduces variation but p value less significant.
# inc.zeros - TRUE = include all zeros in exp_vars for analysis. FALSE = replace all zeros wtih NA
# run_ration - TRUE = it will run correlations for every exp_var1:exp_var2 combination to the response variable. If not "TRUE", will only run correlations with exp_var1 vs res_var
# modification of multi_assoc(method = 'spearman', ). Only divides and multiplys explanitory variables
if (inc.zeros==FALSE){
mdf[,exp_vars] <- apply(mdf[,exp_vars], 2, function(x) ifelse(x == 0, NA, x))
}
mdf$None <- c(rep(1,nrow(mdf)))
exp_vars <- c(exp_vars,match('None', colnames(mdf)))
d <- list()
n <- 0
t=1
col_headers <- c("Explanitory_var1","Explanitory_var2","Response_var", "Estimate", "c.Pval", "Rsq", 'r.Pval')
if(adj.zero == TRUE){ #check to see if adjustment was specified
adj=1
}else{
adj=0
}
for (i in exp_vars[-length(exp_vars)]) { #for every exp variable. This excludes the "None" column added in line above
if ((length(which(mdf[,i]==0|is.na(mdf[,i])==TRUE))<p.zeros*length(mdf[,i])) & (is.numeric(mdf[,i])==TRUE)){ # if the percent of zeros in exp var is less than the specified p.zero threshold, continue, else skip exp var
if (run_ratio == TRUE){
list2 <- exp_vars[-match(i, exp_vars)] #makes a list of all variables except for "i" variable to loop over (for divide only, see multiply below)
} else {
list2 <- 'None'
}
for (j in list2){
if ((length(which(mdf[,j]==0|is.na(mdf[,j])==TRUE))<p.zeros*length(mdf[,j])) & (is.numeric(mdf[,j])==TRUE)){ #check to see if comparison exp var meets p.zero threshold
for (x in res_vars){ #loops over every response variable
#run tests
z <- (mdf[,i]+adj*(0.001*mean(mdf[,i],na.rm = T)))/(mdf[,j]+adj*(0.001*mean(mdf[,j],na.rm = T)))
z[is.infinite(z)] <- NA
y <- mdf[,x]
tmp <- cor.test(y, z, method = method)
reg <- summary(lm(y~z))
tmp_df <- data.frame(
colnames(mdf[i]),
colnames(mdf[j]),
colnames(mdf[x]),
as.numeric(tmp[4]),
as.numeric(tmp[3]),
as.numeric(reg$r.squared),
as.numeric(try(pf(reg$fstatistic[1],reg$fstatistic[2],reg$fstatistic[3], lower.tail = F),silent = T)),
stringsAsFactors = FALSE)
n <- n+1
d[[n]] <- tmp_df
}
}
}
}
if (t > (length(exp_vars)/100)){ # prints status as a percentage
print(paste(match(i,exp_vars)/length(exp_vars)*100,"%"))
t=1
}else{
t=t+1
}
}
tmp_results <- do.call(rbind,d)
colnames(tmp_results) <- col_headers
tmp_results <- as_data_frame(tmp_results)
tmp_results$c.fdr <- p.adjust(tmp_results$c.Pval)
cp <- match("c.Pval", colnames(tmp_results))
tmp_results <- tmp_results[,c(1:cp, ncol(tmp_results),(cp+1):(ncol(tmp_results)-1))]
tmp_results$r.fdr <- p.adjust(tmp_results$r.Pval)
multi_corr_tbl <- tmp_results[order(tmp_results$c.Pval, decreasing = F),]
rownames(multi_corr_tbl) <- c(1:nrow(multi_corr_tbl))
multi_corr_tbl$index <- c(1:nrow(multi_corr_tbl))
if (run_ratio != TRUE){
multi_corr_tbl <- multi_corr_tbl[,-2]
}
return(multi_corr_tbl)
}
#####################################
#####################################
plot_comprsn <- function (data_tbl,comp_tbl,row,color.by='All samples'){
#Arguments:
# data_tbl - dataframe where the explanitory and response variables are as columns and subjects are rows (same df used in multi_pred_cor())
# comp_tbl - comparison table, this is the output of multi_pred_cor()
# row - the row of the comp_tbl you want to plot (row 1 is the most significant correlation)
# color.by - the col of data_table to use as input for aes(color =). Must specify the col with $.
if (colnames(comp_tbl[2]) != "Explanitory_var2"){
tops <- c(comp_tbl[[row,1]], "", comp_tbl[[row,2]])
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
tops <- c(comp_tbl[[row,1]], comp_tbl[[row,2]], comp_tbl[[row,3]])
if (tops[2]=="None"){
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
z <- do.call('/',list(data_tbl[[tops[1]]],data_tbl[[tops[2]]]))
z[is.infinite(z)] <- NA
label <- paste(tops[1], tops[2], sep = paste("_##","/","##_"))
}
}
y <- tops[3]
data_tbl$zn <- z
x <- 'zn'
my.formula <- x~y
if (color.by != "All samples"){
color.by <- data_tbl[,color.by]
colors <- scale_color_brewer(palette="Set1")
} else {
colors <- scale_color_manual(values = 'black')
}
plot <- data_tbl %>% ggplot(aes(x = data_tbl$zn, y = data_tbl[,y], color=color.by))+
xlab(label)+
ylab(tops[3])+
geom_point()+
colors+
stat_cor(method = 'spearman', label.y.npc = .95)+
geom_smooth(method=lm, se=F)
return(plot)
}#Scaterplot and heatmap ##BMI
#Try each phenotype of the disease: phe = c(6:12,14,15)
phe = c(5) #BMI
#phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"
## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"
## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"
## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 2 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
##TRIG
phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 73 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing non-finite values (stat_smooth).## Warning: Removed 73 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 88 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 88 rows containing non-finite values (stat_smooth).## Warning: Removed 88 rows containing missing values (geom_point).
##HDL
phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##Systolic
phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##Diastolic
phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##GLU
phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##GH
phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##AGE
phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
##Waist
phe = c(14) #Waist
taxa = c(20:38) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "5 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20 %"
## [1] "25 %"
## [1] "30 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50 %"
## [1] "55 %"
## [1] "60 %"
## [1] "65 %"
## [1] "70 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80 %"
## [1] "85 %"
## [1] "90 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:39) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "4.76190476190476 %"
## [1] "9.52380952380952 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "14.2857142857143 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.047619047619 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.8095238095238 %"
## [1] "28.5714285714286 %"
## [1] "33.3333333333333 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.0952380952381 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.6190476190476 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.3809523809524 %"
## [1] "57.1428571428571 %"
## [1] "61.9047619047619 %"
## [1] "66.6666666666667 %"
## [1] "71.4285714285714 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.1904761904762 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.9523809523809 %"
## [1] "85.7142857142857 %"
## [1] "90.4761904761905 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "95.2380952380952 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
#Heatmap_level3
#Male
library(corrplot)
res = cor.mtest(merged.table_male[,c(5:11,13,14,20:38)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_male[,c(5:11,13,14,20:38)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_male[, c(5:11, 13, 14, 20:38)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Female
library(corrplot)
res = cor.mtest(merged.table_female[,c(5:11,13,14,20:39)], conf.level = 0.95, rm.na = T)
Correlation <- round(cor(merged.table_female[,c(5:11,13,14,20:30)],method = "spearman", use="complete.obs"), 2)
corrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Read dataframes_level4_order
#ASVs <- read_qza("microbiome_data/filtered/table_filt.qza") #Gives me an error so I commented out.
taxa_table <- read.csv("level-4_order_taxtable_clean.csv", header=T) # csv obtained from /Users/giovanna/OneDrive - UW-Madison/Rotations/3 Denu-Rey/SHOW data/microbiome_data/taxa/taxa_barplot.qza
taxa_table = taxa_table[,1:35] #all rows, from columns 1 to 35
taxa_ra <- sweep(taxa_table[,2:ncol(taxa_table)],1,rowSums(taxa_table[,2:ncol(taxa_table)]),"/") #relative abundance - normalization of sample reads
taxa_ra$index = taxa_table$index
merged.table_order <- merge(MetS_feature, taxa_ra, by.x="16S_ID", by.y="index")
merged.table_male <- merged.table_order %>%
filter(GENDER == "[1] Male")
merged.table_female <- merged.table_order %>%
filter(GENDER == "[2] Female")
#Regression
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
reg_data_order <- merged.table_order %>% select(MetS,AGE_CONSENT, ANT_BMI, PMRC_LAB_TRIG, PMRC_LAB_HDLCHOL, BP_SYSTOLIC_23, BP_DIASTOLIC_23, PMRC_LAB_GLU, PMRC_LAB_GH, ANT_MEAS_WAIST_CM, starts_with("D_0__"))
lmod <- glm(MetS ~ ., family = "binomial", data = reg_data_order)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(lmod)##
## Call:
## glm(formula = MetS ~ ., family = "binomial", data = reg_data_order)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.28435 -0.48984 -0.00012 0.28948 2.18997
##
## Coefficients: (4 not defined because of singularities)
## Estimate
## (Intercept) 3.993e+01
## AGE_CONSENT -1.143e-01
## ANT_BMI -6.735e-02
## PMRC_LAB_TRIG 6.895e-03
## PMRC_LAB_HDLCHOL -3.721e-02
## BP_SYSTOLIC_23 3.423e-02
## BP_DIASTOLIC_23 -5.865e-02
## PMRC_LAB_GLU 4.642e-02
## PMRC_LAB_GH 1.510e+00
## ANT_MEAS_WAIST_CM -5.964e-02
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales 6.198e+01
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales -6.287e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales -8.854e+01
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales -1.181e+02
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales 4.103e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales -5.475e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales 5.004e+01
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales -3.461e+01
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales -5.806e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales 2.723e+03
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales -1.457e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales -1.845e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales 5.572e+00
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales -4.160e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__ -1.798e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales -3.740e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales 3.574e+01
## D_0__Bacteria.D_1__Firmicutes.__.__ 1.296e+04
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales 7.815e+02
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales -8.763e+04
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales -1.599e+03
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales -2.499e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales -1.851e+03
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales -1.037e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales -5.234e+01
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales -9.168e+01
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales -1.618e+03
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales 1.463e+00
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales 2.061e+03
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39 -3.565e+02
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales NA
## Std. Error
## (Intercept) 2.731e+01
## AGE_CONSENT 4.070e-02
## ANT_BMI 1.277e-01
## PMRC_LAB_TRIG 4.737e-03
## PMRC_LAB_HDLCHOL 2.792e-02
## BP_SYSTOLIC_23 3.275e-02
## BP_DIASTOLIC_23 6.103e-02
## PMRC_LAB_GLU 2.347e-02
## PMRC_LAB_GH 1.152e+00
## ANT_MEAS_WAIST_CM 6.227e-02
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales 6.465e+01
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales 1.235e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales 1.716e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales 3.995e+01
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales 5.331e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales 3.435e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales 1.542e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales 2.559e+01
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales 7.419e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales 1.458e+03
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales 8.122e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales 7.094e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales 3.344e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales 2.572e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__ 1.226e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales 3.376e+01
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales 3.349e+01
## D_0__Bacteria.D_1__Firmicutes.__.__ 6.480e+03
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales 3.435e+02
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales 1.877e+07
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales 7.612e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales 1.766e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales 7.148e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales 7.250e+01
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales 2.703e+01
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales 4.859e+02
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales 1.414e+04
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales 9.044e+02
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales 1.907e+03
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39 2.954e+02
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales NA
## z value
## (Intercept) 1.462
## AGE_CONSENT -2.807
## ANT_BMI -0.527
## PMRC_LAB_TRIG 1.456
## PMRC_LAB_HDLCHOL -1.333
## BP_SYSTOLIC_23 1.045
## BP_DIASTOLIC_23 -0.961
## PMRC_LAB_GLU 1.978
## PMRC_LAB_GH 1.311
## ANT_MEAS_WAIST_CM -0.958
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales 0.959
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales -0.509
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales -0.052
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales -2.957
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales 0.008
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales -1.594
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales 0.324
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales -1.352
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales -0.008
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales 1.868
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales -0.179
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales -0.260
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales 0.167
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales -1.618
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__ -0.147
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales -1.108
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales 1.067
## D_0__Bacteria.D_1__Firmicutes.__.__ 2.001
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales 2.276
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales -0.005
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales -2.100
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales -1.415
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales -2.589
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales -1.430
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales -1.936
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales -0.189
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales -0.114
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales 0.002
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales 1.081
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39 -1.207
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales NA
## Pr(>|z|)
## (Intercept) 0.14362
## AGE_CONSENT 0.00500
## ANT_BMI 0.59799
## PMRC_LAB_TRIG 0.14552
## PMRC_LAB_HDLCHOL 0.18264
## BP_SYSTOLIC_23 0.29591
## BP_DIASTOLIC_23 0.33652
## PMRC_LAB_GLU 0.04791
## PMRC_LAB_GH 0.18989
## ANT_MEAS_WAIST_CM 0.33819
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales 0.33769
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales 0.61064
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales 0.95886
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales 0.00311
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales 0.99386
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales 0.11096
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales 0.74562
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales 0.17622
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales 0.99376
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales 0.06170
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales 0.85760
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales 0.79482
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales 0.86767
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales 0.10574
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__ 0.88339
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales 0.26802
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales 0.28582
## D_0__Bacteria.D_1__Firmicutes.__.__ 0.04542
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales 0.02287
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales 0.99628
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales 0.03570
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales 0.15701
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales 0.00961
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales 0.15277
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales 0.05285
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales 0.85034
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales 0.90895
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales 0.99871
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales 0.27986
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39 0.22751
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales NA
##
## (Intercept)
## AGE_CONSENT **
## ANT_BMI
## PMRC_LAB_TRIG
## PMRC_LAB_HDLCHOL
## BP_SYSTOLIC_23
## BP_DIASTOLIC_23
## PMRC_LAB_GLU *
## PMRC_LAB_GH
## ANT_MEAS_WAIST_CM
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales **
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales .
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales
## D_0__Bacteria.D_1__Firmicutes.__.__ *
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales *
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales *
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales **
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales .
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 167.07 on 120 degrees of freedom
## Residual deviance: 74.70 on 81 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 154.7
##
## Number of Fisher Scoring iterations: 18#Male
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_male <- colSums(Filter(is.numeric,merged.table_male))
zero_cols <- names(colsums_male[colsums_male==0 & !is.na(colsums_male)])
# re-define merged.tale without cols that sum to zero
merged.table_male <- merged.table_male %>% select(-one_of(zero_cols))
#Female
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_female <- colSums(Filter(is.numeric,merged.table_female))
zero_cols <- names(colsums_female[colsums_female==0 & !is.na(colsums_female)])
# re-define merged.tale without cols that sum to zero
merged.table_female <- merged.table_female %>% select(-one_of(zero_cols))#Correlation test
multi_assoc <- function(mdf,exp_vars, res_vars, p.zeros=.25,adj.zero=TRUE,method, inc.zeros=TRUE, run_ratio = TRUE){
#Arguments:
# mdf - dataframe where the explanitory and response variables are as columns and subjects are rows
# exp_vars - a vector of numbers that indicate the explanitory variable cols in mdf
# res_vars - a vector of numbers that indicate the response variable cols in mdf
# p.zeros - the max amount of zeros in a singe variable that will be tolerated (expressed as a percent)
# this taxa has to be 25% present in all samples to run the analysis.
# adj.zeros - add a small value to each datapoint (0.1% of the variable average) to allow for divide by zero
# add small number, so it's close to 0 but has some values (if running correlation with 0, causes problems)
# method - 'spearman', 'pearson'
# spearman brings values high to low [ranking]; whereas pearson uses the actual values; spearman corrects/reduces variation but p value less significant.
# inc.zeros - TRUE = include all zeros in exp_vars for analysis. FALSE = replace all zeros wtih NA
# run_ration - TRUE = it will run correlations for every exp_var1:exp_var2 combination to the response variable. If not "TRUE", will only run correlations with exp_var1 vs res_var
# modification of multi_assoc(method = 'spearman', ). Only divides and multiplys explanitory variables
if (inc.zeros==FALSE){
mdf[,exp_vars] <- apply(mdf[,exp_vars], 2, function(x) ifelse(x == 0, NA, x))
}
mdf$None <- c(rep(1,nrow(mdf)))
exp_vars <- c(exp_vars,match('None', colnames(mdf)))
d <- list()
n <- 0
t=1
col_headers <- c("Explanitory_var1","Explanitory_var2","Response_var", "Estimate", "c.Pval", "Rsq", 'r.Pval')
if(adj.zero == TRUE){ #check to see if adjustment was specified
adj=1
}else{
adj=0
}
for (i in exp_vars[-length(exp_vars)]) { #for every exp variable. This excludes the "None" column added in line above
if ((length(which(mdf[,i]==0|is.na(mdf[,i])==TRUE))<p.zeros*length(mdf[,i])) & (is.numeric(mdf[,i])==TRUE)){ # if the percent of zeros in exp var is less than the specified p.zero threshold, continue, else skip exp var
if (run_ratio == TRUE){
list2 <- exp_vars[-match(i, exp_vars)] #makes a list of all variables except for "i" variable to loop over (for divide only, see multiply below)
} else {
list2 <- 'None'
}
for (j in list2){
if ((length(which(mdf[,j]==0|is.na(mdf[,j])==TRUE))<p.zeros*length(mdf[,j])) & (is.numeric(mdf[,j])==TRUE)){ #check to see if comparison exp var meets p.zero threshold
for (x in res_vars){ #loops over every response variable
#run tests
z <- (mdf[,i]+adj*(0.001*mean(mdf[,i],na.rm = T)))/(mdf[,j]+adj*(0.001*mean(mdf[,j],na.rm = T)))
z[is.infinite(z)] <- NA
y <- mdf[,x]
tmp <- cor.test(y, z, method = method)
reg <- summary(lm(y~z))
tmp_df <- data.frame(
colnames(mdf[i]),
colnames(mdf[j]),
colnames(mdf[x]),
as.numeric(tmp[4]),
as.numeric(tmp[3]),
as.numeric(reg$r.squared),
as.numeric(try(pf(reg$fstatistic[1],reg$fstatistic[2],reg$fstatistic[3], lower.tail = F),silent = T)),
stringsAsFactors = FALSE)
n <- n+1
d[[n]] <- tmp_df
}
}
}
}
if (t > (length(exp_vars)/100)){ # prints status as a percentage
print(paste(match(i,exp_vars)/length(exp_vars)*100,"%"))
t=1
}else{
t=t+1
}
}
tmp_results <- do.call(rbind,d)
colnames(tmp_results) <- col_headers
tmp_results <- as_data_frame(tmp_results)
tmp_results$c.fdr <- p.adjust(tmp_results$c.Pval)
cp <- match("c.Pval", colnames(tmp_results))
tmp_results <- tmp_results[,c(1:cp, ncol(tmp_results),(cp+1):(ncol(tmp_results)-1))]
tmp_results$r.fdr <- p.adjust(tmp_results$r.Pval)
multi_corr_tbl <- tmp_results[order(tmp_results$c.Pval, decreasing = F),]
rownames(multi_corr_tbl) <- c(1:nrow(multi_corr_tbl))
multi_corr_tbl$index <- c(1:nrow(multi_corr_tbl))
if (run_ratio != TRUE){
multi_corr_tbl <- multi_corr_tbl[,-2]
}
return(multi_corr_tbl)
}
#####################################
#####################################
plot_comprsn <- function (data_tbl,comp_tbl,row,color.by='All samples'){
#Arguments:
# data_tbl - dataframe where the explanitory and response variables are as columns and subjects are rows (same df used in multi_pred_cor())
# comp_tbl - comparison table, this is the output of multi_pred_cor()
# row - the row of the comp_tbl you want to plot (row 1 is the most significant correlation)
# color.by - the col of data_table to use as input for aes(color =). Must specify the col with $.
if (colnames(comp_tbl[2]) != "Explanitory_var2"){
tops <- c(comp_tbl[[row,1]], "", comp_tbl[[row,2]])
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
tops <- c(comp_tbl[[row,1]], comp_tbl[[row,2]], comp_tbl[[row,3]])
if (tops[2]=="None"){
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
z <- do.call('/',list(data_tbl[[tops[1]]],data_tbl[[tops[2]]]))
z[is.infinite(z)] <- NA
label <- paste(tops[1], tops[2], sep = paste("_##","/","##_"))
}
}
y <- tops[3]
data_tbl$zn <- z
x <- 'zn'
my.formula <- x~y
if (color.by != "All samples"){
color.by <- data_tbl[,color.by]
colors <- scale_color_brewer(palette="Set1")
} else {
colors <- scale_color_manual(values = 'black')
}
plot <- data_tbl %>% ggplot(aes(x = data_tbl$zn, y = data_tbl[,y], color=color.by))+
xlab(label)+
ylab(tops[3])+
geom_point()+
colors+
stat_cor(method = 'spearman', label.y.npc = .95)+
geom_smooth(method=lm, se=F)
return(plot)
}#Scaterplot and heatmap ##BMI
#Try each phenotype of the disease: phe = c(6:12,14,15)
phe = c(5) #BMI
#phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"
## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"
## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"
## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 2 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
##TRIG
phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 73 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing non-finite values (stat_smooth).## Warning: Removed 73 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 88 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 88 rows containing non-finite values (stat_smooth).## Warning: Removed 88 rows containing missing values (geom_point).
##HDL
phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##Systolic
phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##Diastolic
phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##GLU
phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##GH
phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##AGE
phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
##Waist
phe = c(14) #Waist
taxa = c(20:51) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "9.09090909090909 %"
## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"
## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6969696969697 %"
## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:51) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "3.03030303030303 %"
## [1] "6.06060606060606 %"
## [1] "9.09090909090909 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "12.1212121212121 %"
## [1] "15.1515151515152 %"
## [1] "18.1818181818182 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "21.2121212121212 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.2424242424242 %"
## [1] "27.2727272727273 %"
## [1] "30.3030303030303 %"
## [1] "33.3333333333333 %"
## [1] "36.3636363636364 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3939393939394 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.4242424242424 %"
## [1] "45.4545454545455 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.4848484848485 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "51.5151515151515 %"
## [1] "54.5454545454545 %"
## [1] "57.5757575757576 %"
## [1] "60.6060606060606 %"
## [1] "63.6363636363636 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.6666666666667 %"
## [1] "69.6969696969697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "72.7272727272727 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.7575757575758 %"
## [1] "78.7878787878788 %"
## [1] "81.8181818181818 %"
## [1] "84.8484848484848 %"
## [1] "87.8787878787879 %"
## [1] "90.9090909090909 %"
## [1] "93.9393939393939 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "96.969696969697 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
#Heatmap_level4
#Male
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_male[,c(5:11,13,14,20:51)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_male[,c(5:11,13,14,20:51)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_male[, c(5:11, 13, 14, 20:51)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Female
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_female[,c(5:11,13,14,20:51)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_female[,c(5:11,13,14,20:51)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_female[, c(5:11, 13, 14, 20:51)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Read dataframes_level5_family
#ASVs <- read_qza("microbiome_data/filtered/table_filt.qza") #Gives me an error so I commented out.
taxa_table <- read.csv("level-5_family_taxtable_clean.csv", header=T) # csv obtained from /Users/giovanna/OneDrive - UW-Madison/Rotations/3 Denu-Rey/SHOW data/microbiome_data/taxa/taxa_barplot.qza
taxa_table = taxa_table[,1:75] #all rows, from columns 1 to 112
taxa_ra <- sweep(taxa_table[,2:ncol(taxa_table)],1,rowSums(taxa_table[,2:ncol(taxa_table)]),"/") #relative abundance - normalization of sample reads
taxa_ra$index = taxa_table$index
merged.table_family <- merge(MetS_feature, taxa_ra, by.x="16S_ID", by.y="index")
merged.table_male <- merged.table_family %>%
filter(GENDER == "[1] Male")
merged.table_female <- merged.table_family %>%
filter(GENDER == "[2] Female")
#Regression
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
reg_data_family <- merged.table_family %>% select(MetS,AGE_CONSENT, ANT_BMI, PMRC_LAB_TRIG, PMRC_LAB_HDLCHOL, BP_SYSTOLIC_23, BP_DIASTOLIC_23, PMRC_LAB_GLU, PMRC_LAB_GH, ANT_MEAS_WAIST_CM, starts_with("D_0__"))
lmod <- glm(MetS ~ ., family = "binomial", data = reg_data_family)## Warning: glm.fit: algorithm did not converge## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(lmod)##
## Call:
## glm(formula = MetS ~ ., family = "binomial", data = reg_data_family)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.973e-05 -2.892e-06 -2.110e-08 2.409e-06 2.716e-05
##
## Coefficients: (8 not defined because of singularities)
## Estimate
## (Intercept) 9.295e+02
## AGE_CONSENT -2.792e+00
## ANT_BMI 1.765e+00
## PMRC_LAB_TRIG 2.005e-01
## PMRC_LAB_HDLCHOL -4.659e-01
## BP_SYSTOLIC_23 1.950e+00
## BP_DIASTOLIC_23 -1.399e+00
## PMRC_LAB_GLU 6.920e-01
## PMRC_LAB_GH 4.136e+01
## ANT_MEAS_WAIST_CM -3.022e+00
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae -1.122e+03
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae 1.771e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae -9.529e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae -3.836e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae 4.450e+07
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae -1.338e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae -2.202e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae 9.764e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis -2.599e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae 1.440e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured 2.170e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae -8.176e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae -5.753e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae -1.187e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae 1.713e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae 6.449e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae -8.640e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae -4.203e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae -8.322e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae -1.410e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured -3.315e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__ 2.825e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae -6.014e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306 1.028e+07
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium 1.621e+05
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__ 4.408e+04
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae 1.670e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI 1.816e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae 4.079e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae -6.464e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae -4.128e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae -4.342e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae -6.730e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae 2.214e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae -3.710e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1 9.501e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group 5.538e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae 1.360e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae 1.672e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI 2.499e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII -7.090e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae -1.017e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae -1.817e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae -3.467e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae -1.139e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__ 1.565e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__ -3.066e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae -3.446e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae -1.652e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae -9.438e+02
## D_0__Bacteria.D_1__Firmicutes.__.__.__ 1.864e+05
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae 1.603e+04
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae -1.106e+05
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured -2.654e+04
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae -5.143e+03
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae -2.522e+04
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae -1.467e+03
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae -1.524e+03
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae -1.206e+04
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae -2.895e+06
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae -5.786e+03
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism -6.409e+04
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822 -5.906e+05
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome 1.094e+05
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium -1.082e+05
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__ 4.723e+04
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae NA
## Std. Error
## (Intercept) 3.362e+06
## AGE_CONSENT 4.754e+03
## ANT_BMI 1.519e+04
## PMRC_LAB_TRIG 5.303e+02
## PMRC_LAB_HDLCHOL 2.997e+03
## BP_SYSTOLIC_23 5.122e+03
## BP_DIASTOLIC_23 8.027e+03
## PMRC_LAB_GLU 2.082e+03
## PMRC_LAB_GH 1.709e+05
## ANT_MEAS_WAIST_CM 8.482e+03
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae 1.088e+07
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae 2.885e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae 1.190e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae 5.301e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae 7.048e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae 4.209e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae 5.449e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae 1.682e+07
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis 1.634e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae 6.579e+07
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured 6.005e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae 3.123e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae 1.366e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae 3.084e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae 6.005e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae 4.093e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae 4.855e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae 3.316e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae 2.851e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae 4.689e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured 7.316e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__ 6.074e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae 1.023e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306 2.723e+10
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium 8.819e+08
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__ 3.984e+08
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae 4.421e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI 9.198e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae 2.275e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae 1.459e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae 8.947e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae 6.822e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae 2.244e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae 4.641e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae 1.146e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1 5.164e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group 1.715e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae 5.598e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae 4.344e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI 2.215e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII 2.129e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae 2.885e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae 4.566e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae 1.437e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae 2.819e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__ 1.344e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__ 1.272e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae 3.056e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae 5.349e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae 3.046e+06
## D_0__Bacteria.D_1__Firmicutes.__.__.__ 9.960e+08
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae 6.791e+07
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae 1.353e+09
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured 2.072e+08
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae 2.148e+07
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae 1.901e+08
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae 7.008e+06
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae 3.593e+06
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae 2.909e+07
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae 7.311e+09
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae 4.736e+07
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism 1.771e+08
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822 2.954e+09
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome 2.264e+08
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium 5.634e+08
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__ 2.396e+08
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae NA
## z value
## (Intercept) 0.000
## AGE_CONSENT -0.001
## ANT_BMI 0.000
## PMRC_LAB_TRIG 0.000
## PMRC_LAB_HDLCHOL 0.000
## BP_SYSTOLIC_23 0.000
## BP_DIASTOLIC_23 0.000
## PMRC_LAB_GLU 0.000
## PMRC_LAB_GH 0.000
## ANT_MEAS_WAIST_CM 0.000
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae 0.000
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae -0.001
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae 0.001
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae 0.001
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae 0.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__ 0.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae -0.001
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306 0.000
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium 0.000
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__ 0.000
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__ 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__ 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae 0.000
## D_0__Bacteria.D_1__Firmicutes.__.__.__ 0.000
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae 0.000
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae 0.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae 0.000
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae 0.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism 0.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822 0.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome 0.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium 0.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__ 0.000
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae NA
## Pr(>|z|)
## (Intercept) 1.000
## AGE_CONSENT 1.000
## ANT_BMI 1.000
## PMRC_LAB_TRIG 1.000
## PMRC_LAB_HDLCHOL 1.000
## BP_SYSTOLIC_23 1.000
## BP_DIASTOLIC_23 1.000
## PMRC_LAB_GLU 1.000
## PMRC_LAB_GH 1.000
## ANT_MEAS_WAIST_CM 1.000
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae 1.000
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae 0.999
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae 0.999
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae 1.000
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__ 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae 1.000
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306 1.000
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium 1.000
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__ 1.000
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__ 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__ 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae 1.000
## D_0__Bacteria.D_1__Firmicutes.__.__.__ 1.000
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae 1.000
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae 1.000
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae 1.000
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae 1.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism 1.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822 1.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome 1.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium 1.000
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__ 1.000
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae NA
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.6707e+02 on 120 degrees of freedom
## Residual deviance: 6.5603e-09 on 45 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 152
##
## Number of Fisher Scoring iterations: 25#Male
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_male <- colSums(Filter(is.numeric,merged.table_male))
zero_cols <- names(colsums_male[colsums_male==0 & !is.na(colsums_male)])
# re-define merged.tale without cols that sum to zero
merged.table_male <- merged.table_male %>% select(-one_of(zero_cols))
#Female
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_female <- colSums(Filter(is.numeric,merged.table_female))
zero_cols <- names(colsums_female[colsums_female==0 & !is.na(colsums_female)])
# re-define merged.tale without cols that sum to zero
merged.table_female <- merged.table_female %>% select(-one_of(zero_cols))#Correlation test
multi_assoc <- function(mdf,exp_vars, res_vars, p.zeros=.25,adj.zero=TRUE,method, inc.zeros=TRUE, run_ratio = TRUE){
#Arguments:
# mdf - dataframe where the explanitory and response variables are as columns and subjects are rows
# exp_vars - a vector of numbers that indicate the explanitory variable cols in mdf
# res_vars - a vector of numbers that indicate the response variable cols in mdf
# p.zeros - the max amount of zeros in a singe variable that will be tolerated (expressed as a percent)
# this taxa has to be 25% present in all samples to run the analysis.
# adj.zeros - add a small value to each datapoint (0.1% of the variable average) to allow for divide by zero
# add small number, so it's close to 0 but has some values (if running correlation with 0, causes problems)
# method - 'spearman', 'pearson'
# spearman brings values high to low [ranking]; whereas pearson uses the actual values; spearman corrects/reduces variation but p value less significant.
# inc.zeros - TRUE = include all zeros in exp_vars for analysis. FALSE = replace all zeros wtih NA
# run_ration - TRUE = it will run correlations for every exp_var1:exp_var2 combination to the response variable. If not "TRUE", will only run correlations with exp_var1 vs res_var
# modification of multi_assoc(method = 'spearman', ). Only divides and multiplys explanitory variables
if (inc.zeros==FALSE){
mdf[,exp_vars] <- apply(mdf[,exp_vars], 2, function(x) ifelse(x == 0, NA, x))
}
mdf$None <- c(rep(1,nrow(mdf)))
exp_vars <- c(exp_vars,match('None', colnames(mdf)))
d <- list()
n <- 0
t=1
col_headers <- c("Explanitory_var1","Explanitory_var2","Response_var", "Estimate", "c.Pval", "Rsq", 'r.Pval')
if(adj.zero == TRUE){ #check to see if adjustment was specified
adj=1
}else{
adj=0
}
for (i in exp_vars[-length(exp_vars)]) { #for every exp variable. This excludes the "None" column added in line above
if ((length(which(mdf[,i]==0|is.na(mdf[,i])==TRUE))<p.zeros*length(mdf[,i])) & (is.numeric(mdf[,i])==TRUE)){ # if the percent of zeros in exp var is less than the specified p.zero threshold, continue, else skip exp var
if (run_ratio == TRUE){
list2 <- exp_vars[-match(i, exp_vars)] #makes a list of all variables except for "i" variable to loop over (for divide only, see multiply below)
} else {
list2 <- 'None'
}
for (j in list2){
if ((length(which(mdf[,j]==0|is.na(mdf[,j])==TRUE))<p.zeros*length(mdf[,j])) & (is.numeric(mdf[,j])==TRUE)){ #check to see if comparison exp var meets p.zero threshold
for (x in res_vars){ #loops over every response variable
#run tests
z <- (mdf[,i]+adj*(0.001*mean(mdf[,i],na.rm = T)))/(mdf[,j]+adj*(0.001*mean(mdf[,j],na.rm = T)))
z[is.infinite(z)] <- NA
y <- mdf[,x]
tmp <- cor.test(y, z, method = method)
reg <- summary(lm(y~z))
tmp_df <- data.frame(
colnames(mdf[i]),
colnames(mdf[j]),
colnames(mdf[x]),
as.numeric(tmp[4]),
as.numeric(tmp[3]),
as.numeric(reg$r.squared),
as.numeric(try(pf(reg$fstatistic[1],reg$fstatistic[2],reg$fstatistic[3], lower.tail = F),silent = T)),
stringsAsFactors = FALSE)
n <- n+1
d[[n]] <- tmp_df
}
}
}
}
if (t > (length(exp_vars)/100)){ # prints status as a percentage
print(paste(match(i,exp_vars)/length(exp_vars)*100,"%"))
t=1
}else{
t=t+1
}
}
tmp_results <- do.call(rbind,d)
colnames(tmp_results) <- col_headers
tmp_results <- as_data_frame(tmp_results)
tmp_results$c.fdr <- p.adjust(tmp_results$c.Pval)
cp <- match("c.Pval", colnames(tmp_results))
tmp_results <- tmp_results[,c(1:cp, ncol(tmp_results),(cp+1):(ncol(tmp_results)-1))]
tmp_results$r.fdr <- p.adjust(tmp_results$r.Pval)
multi_corr_tbl <- tmp_results[order(tmp_results$c.Pval, decreasing = F),]
rownames(multi_corr_tbl) <- c(1:nrow(multi_corr_tbl))
multi_corr_tbl$index <- c(1:nrow(multi_corr_tbl))
if (run_ratio != TRUE){
multi_corr_tbl <- multi_corr_tbl[,-2]
}
return(multi_corr_tbl)
}
#####################################
#####################################
plot_comprsn <- function (data_tbl,comp_tbl,row,color.by='All samples'){
#Arguments:
# data_tbl - dataframe where the explanitory and response variables are as columns and subjects are rows (same df used in multi_pred_cor())
# comp_tbl - comparison table, this is the output of multi_pred_cor()
# row - the row of the comp_tbl you want to plot (row 1 is the most significant correlation)
# color.by - the col of data_table to use as input for aes(color =). Must specify the col with $.
if (colnames(comp_tbl[2]) != "Explanitory_var2"){
tops <- c(comp_tbl[[row,1]], "", comp_tbl[[row,2]])
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
tops <- c(comp_tbl[[row,1]], comp_tbl[[row,2]], comp_tbl[[row,3]])
if (tops[2]=="None"){
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
z <- do.call('/',list(data_tbl[[tops[1]]],data_tbl[[tops[2]]]))
z[is.infinite(z)] <- NA
label <- paste(tops[1], tops[2], sep = paste("_##","/","##_"))
}
}
y <- tops[3]
data_tbl$zn <- z
x <- 'zn'
my.formula <- x~y
if (color.by != "All samples"){
color.by <- data_tbl[,color.by]
colors <- scale_color_brewer(palette="Set1")
} else {
colors <- scale_color_manual(values = 'black')
}
plot <- data_tbl %>% ggplot(aes(x = data_tbl$zn, y = data_tbl[,y], color=color.by))+
xlab(label)+
ylab(tops[3])+
geom_point()+
colors+
stat_cor(method = 'spearman', label.y.npc = .95)+
geom_smooth(method=lm, se=F)
return(plot)
}#Scaterplot and heatmap ##BMI
#Try each phenotype of the disease: phe = c(6:12,14,15)
phe = c(5) #BMI
#phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"
## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"
## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"
## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"
## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"
## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 2 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
##TRIG
phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 73 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing non-finite values (stat_smooth).## Warning: Removed 73 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 88 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 88 rows containing non-finite values (stat_smooth).## Warning: Removed 88 rows containing missing values (geom_point).
##HDL
phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##Systolic
phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##Diastolic
phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##GLU
phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##GH
phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##AGE
phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
##Waist
phe = c(14) #Waist
taxa = c(20:87) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.44927536231884 %"
## [1] "2.89855072463768 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.34782608695652 %"
## [1] "5.79710144927536 %"
## [1] "7.2463768115942 %"
## [1] "8.69565217391304 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.1449275362319 %"
## [1] "11.5942028985507 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.0434782608696 %"
## [1] "14.4927536231884 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "15.9420289855072 %"
## [1] "17.3913043478261 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.8405797101449 %"
## [1] "20.2898550724638 %"
## [1] "21.7391304347826 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "23.1884057971014 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "24.6376811594203 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.0869565217391 %"
## [1] "27.536231884058 %"
## [1] "28.9855072463768 %"
## [1] "30.4347826086957 %"
## [1] "31.8840579710145 %"
## [1] "33.3333333333333 %"
## [1] "34.7826086956522 %"
## [1] "36.231884057971 %"
## [1] "37.6811594202899 %"
## [1] "39.1304347826087 %"
## [1] "40.5797101449275 %"
## [1] "42.0289855072464 %"
## [1] "43.4782608695652 %"
## [1] "44.9275362318841 %"
## [1] "46.3768115942029 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.8260869565217 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.2753623188406 %"
## [1] "50.7246376811594 %"
## [1] "52.1739130434783 %"
## [1] "53.6231884057971 %"
## [1] "55.0724637681159 %"
## [1] "56.5217391304348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.9710144927536 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.4202898550725 %"
## [1] "60.8695652173913 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.3188405797101 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.768115942029 %"
## [1] "65.2173913043478 %"
## [1] "66.6666666666667 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.1159420289855 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.5652173913043 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.0144927536232 %"
## [1] "72.463768115942 %"
## [1] "73.9130434782609 %"
## [1] "75.3623188405797 %"
## [1] "76.8115942028985 %"
## [1] "78.2608695652174 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.7101449275362 %"
## [1] "81.1594202898551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "82.6086956521739 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.0579710144928 %"
## [1] "85.5072463768116 %"
## [1] "86.9565217391304 %"
## [1] "88.4057971014493 %"
## [1] "89.8550724637681 %"
## [1] "91.304347826087 %"
## [1] "92.7536231884058 %"
## [1] "94.2028985507246 %"
## [1] "95.6521739130435 %"
## [1] "97.1014492753623 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.5507246376812 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:91) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.36986301369863 %"
## [1] "2.73972602739726 %"
## [1] "4.10958904109589 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "5.47945205479452 %"
## [1] "6.84931506849315 %"
## [1] "8.21917808219178 %"
## [1] "9.58904109589041 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.958904109589 %"
## [1] "12.3287671232877 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "13.6986301369863 %"
## [1] "15.0684931506849 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "16.4383561643836 %"
## [1] "17.8082191780822 %"
## [1] "19.1780821917808 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "20.5479452054795 %"
## [1] "21.9178082191781 %"
## [1] "23.2876712328767 %"
## [1] "24.6575342465753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.027397260274 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.3972602739726 %"
## [1] "28.7671232876712 %"
## [1] "30.1369863013699 %"
## [1] "31.5068493150685 %"
## [1] "32.8767123287671 %"
## [1] "34.2465753424658 %"
## [1] "35.6164383561644 %"
## [1] "36.986301369863 %"
## [1] "38.3561643835616 %"
## [1] "39.7260273972603 %"
## [1] "41.0958904109589 %"
## [1] "42.4657534246575 %"
## [1] "43.8356164383562 %"
## [1] "45.2054794520548 %"
## [1] "46.5753424657534 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "47.945205479452 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.3150684931507 %"
## [1] "50.6849315068493 %"
## [1] "52.0547945205479 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.4246575342466 %"
## [1] "54.7945205479452 %"
## [1] "56.1643835616438 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.5342465753425 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "58.9041095890411 %"
## [1] "60.2739726027397 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.6438356164384 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.013698630137 %"
## [1] "64.3835616438356 %"
## [1] "65.7534246575342 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.1232876712329 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.4931506849315 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.8630136986301 %"
## [1] "71.2328767123288 %"
## [1] "72.6027397260274 %"
## [1] "73.972602739726 %"
## [1] "75.3424657534247 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.7123287671233 %"
## [1] "78.0821917808219 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.4520547945205 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "80.8219178082192 %"
## [1] "82.1917808219178 %"
## [1] "83.5616438356164 %"
## [1] "84.9315068493151 %"
## [1] "86.3013698630137 %"
## [1] "87.6712328767123 %"
## [1] "89.041095890411 %"
## [1] "90.4109589041096 %"
## [1] "91.7808219178082 %"
## [1] "93.1506849315068 %"
## [1] "94.5205479452055 %"
## [1] "95.8904109589041 %"
## [1] "97.2602739726027 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "98.6301369863014 %"## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
#Heatmap
#Male
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_male[,c(5:11,13,14,20:87)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_male[,c(5:11,13,14,20:87)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_male[, c(5:11, 13, 14, 20:87)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr
#Female
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_female[,c(5:11,13,14,20:91)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_female[,c(5:11,13,14,20:91)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_female[, c(5:11, 13, 14, 20:91)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr## Warning in corrplot(Correlation, method = "ellipse", tl.col = "black", type =
## "upper", : Not been able to calculate text margin, please try again with a clean
## new empty window using {plot.new(); dev.off()} or reduce tl.cex
#Read dataframes_level6_genus
#ASVs <- read_qza("microbiome_data/filtered/table_filt.qza") #Gives me an error so I commented out.
taxa_table <- read.csv("level-6_genus_taxtable_clean.csv", header=T) # csv obtained from /Users/giovanna/OneDrive - UW-Madison/Rotations/3 Denu-Rey/SHOW data/microbiome_data/taxa/taxa_barplot.qza
taxa_table = taxa_table[,1:283] #all rows, from columns 1 to 528
taxa_ra <- sweep(taxa_table[,2:ncol(taxa_table)],1,rowSums(taxa_table[,2:ncol(taxa_table)]),"/") #relative abundance - normalization of sample reads
taxa_ra$index = taxa_table$index
merged.table_genus <- merge(MetS_feature, taxa_ra, by.x="16S_ID", by.y="index")
merged.table_male <- merged.table_genus %>%
filter(GENDER == "[1] Male")
merged.table_female <- merged.table_genus %>%
filter(GENDER == "[2] Female")
#Regression
MetS_feature$MetS <- ifelse((MetS_feature$MetS.binary == 1) | (MetS_feature$gh.binary ==1), 1, 0)
reg_data_genus <- merged.table_genus %>% select(MetS,AGE_CONSENT, ANT_BMI, PMRC_LAB_TRIG, PMRC_LAB_HDLCHOL, BP_SYSTOLIC_23, BP_DIASTOLIC_23, PMRC_LAB_GLU, PMRC_LAB_GH, ANT_MEAS_WAIST_CM, starts_with("D_0__"))
lmod <- glm(MetS ~ ., family = "binomial", data = reg_data_genus)## Warning: glm.fit: algorithm did not convergesummary(lmod)##
## Call:
## glm(formula = MetS ~ ., family = "binomial", data = reg_data_genus)
##
## Deviance Residuals:
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [26] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [51] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [76] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## [101] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## Coefficients: (171 not defined because of singularities)
## Estimate
## (Intercept) -1.344e+03
## AGE_CONSENT 2.023e+00
## ANT_BMI 2.271e-01
## PMRC_LAB_TRIG 5.092e-01
## PMRC_LAB_HDLCHOL 1.102e+01
## BP_SYSTOLIC_23 6.689e+00
## BP_DIASTOLIC_23 -1.114e+01
## PMRC_LAB_GLU -3.481e-01
## PMRC_LAB_GH -6.671e+01
## ANT_MEAS_WAIST_CM 6.863e+00
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanobrevibacter 6.565e+04
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanosphaera 1.485e+06
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae.D_5__Methanomassiliicoccus -4.642e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Actinomyces 1.766e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Mobiluncus -7.485e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Varibaculum -6.721e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae.D_5__Bifidobacterium -1.357e+03
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae.D_5__Lawsonella -1.991e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae.D_5__Rothia -2.179e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Libanicoccus -2.760e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Olsenella -8.906e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae.D_5__Collinsella 2.495e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis.D_5__uncultured 1.236e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Adlercreutzia 9.330e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__CHKCI002 -8.419e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Eggerthella -4.002e+04
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Enterorhabdus -1.092e+06
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Gordonibacter 3.111e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Parvibacter 1.136e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Senegalimassilia 8.262e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Slackia -3.492e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__uncultured 3.079e+05
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured.D_5__uncultured.bacterium -1.757e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae.D_5__Bacteroides 5.217e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Barnesiella 1.806e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Coprobacter 8.954e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__uncultured -3.351e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae.D_5__Dysgonomonas -6.967e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Butyricimonas 4.099e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Odoribacter 3.314e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Sanguibacteroides 2.305e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__Muribaculum NA
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__metagenome 1.160e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.Porphyromonadaceae.bacterium 4.867e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.bacterium 3.051e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.organism 1.856e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.__ -3.712e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae.D_5__Porphyromonas -1.365e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Alloprevotella -1.034e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Paraprevotella -2.344e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella -4.916e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.2 -1.665e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.6 2.277e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.7 -9.853e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.9 6.501e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.NK3B31.group 3.464e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.UCG.001 -4.731e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__uncultured 9.818e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.__ -2.645e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Alistipes -6.964e+02
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Millionella 3.617e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenella 1.014e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenellaceae.RC9.gut.group 8.889e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae.D_5__Parabacteroides -6.853e+03
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__gut.metagenome 5.108e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__uncultured.bacterium -1.008e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__.__ 5.104e+04
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae.D_5__uncultured 3.223e+05
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae.D_5__Sphingobacterium NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111.D_5__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306.D_5__Clostridium.sp..CAG.306 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium.D_5__uncultured.bacterium 9.699e+04
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__.__ 1.000e+04
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae.D_5__Campylobacter 1.036e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI.D_5__Gemella 5.139e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae.D_5__Staphylococcus -4.323e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Carnobacterium -1.033e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Granulicatella -9.153e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae.D_5__Enterococcus 7.750e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Lactobacillus -8.521e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Pediococcus -1.134e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Leuconostoc 2.728e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Weissella 2.375e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Lactococcus -1.341e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Streptococcus -4.927e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Catabacter 5.216e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Christensenellaceae.R.7.group -1.302e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__uncultured -5.307e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.__ -3.388e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.1 -7.043e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.13 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__Clostridiales.bacterium.enrichment.culture.clone.06.1235251.67 -1.172e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__gut.metagenome -1.043e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__metagenome NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.Thermoanaerobacterales.bacterium 7.765e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.bacterium 1.611e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.organism -8.759e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae.D_5__Defluviitaleaceae.UCG.011 7.656e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Anaerofustis -3.745e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Eubacterium 7.774e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Anaerococcus 6.647e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Ezakiella -2.132e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Finegoldia -1.181e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Parvimonas -2.595e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Peptoniphilus 4.456e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__W5053 1.085e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.AD3011.group -2.936e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.UCG.001 -8.012e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Mogibacterium -6.498e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..brachy.group -5.882e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..nodatum.group 3.572e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__uncultured -1.127e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.__ 2.781e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Acetitomaculum -3.742e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Agathobacter 2.712e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerosporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerostipes 2.270e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Blautia -1.135e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Butyrivibrio -1.077e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CAG.56 -1.692e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CHKCI001 -1.170e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cellulosilyticum 6.064e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.1 -1.331e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.2 8.463e+02
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.3 7.596e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cuneatibacter -5.894e+04
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Dorea 3.926e+03
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Eisenbergiella 1.875e+05
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Epulopiscium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Fusicatenibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066575 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066755 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Howardella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Hungatella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.FCS020.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.ND3007.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK3A20.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4A136.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4B4.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.001 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lactonifactor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Marvinbryantia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Moryella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Murimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Oribacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Roseburia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Sellimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Shuttleworthia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.4 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__UC5.1.2E3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..eligens.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..fissicatena.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..hallii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ruminantium.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ventriosum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..xylanophilum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gauvreauii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gnavus.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..torques.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__Peptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Clostridioides NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Intestinibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Paeniclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptostreptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Romboutsia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Terrisporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Acetanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerofilum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerotruncus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Angelakisella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Butyricicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__CAG.352 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Candidatus.Soleaferrea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Caproiciproducens NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__DTU089 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Faecalibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Flavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Fournierella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__GCA.900066225 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Harryflintia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Hydrogenoanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Intestinimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Negativibacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Phocea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Pseudoflavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.5 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.6 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.9 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.NK4A214.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.002 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.005 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.007 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.009 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.013 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.014 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.2 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Subdoligranulum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__UBA1819 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__.Eubacterium..coprostanoligenes.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Candidatus.Stoquefichus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Catenibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Coprobacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Dielma NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelatoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalibaculum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalitalea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemanella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemania NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Merdibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Solobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Turicibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__.Clostridium..innocuum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Acidaminococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Phascolarctobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Succiniclasticum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Allisonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaeroglobus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaerovibrio NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Dialister NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megamonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megasphaera NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Mitsuokella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Veillonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.__.__.__.__ NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Cetobacterium NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Fusobacterium NA
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae.D_5__Victivallis NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__Azospirillum.sp..47_25 NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Bilophila NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Desulfovibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Mailhella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae.D_5__Succinivibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Comamonas NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Parasutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Sutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Escherichia.Shigella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Klebsiella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Proteus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae.D_5__Haemophilus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae.D_5__Pseudomonas NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Cloacibacillus NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Fretibacterium NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Pyramidobacter NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism.D_5__uncultured.organism NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822.D_5__Firmicutes.bacterium.CAG.822 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202.D_5__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39.D_5__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__.__ NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae.D_5__Akkermansia NA
## Std. Error
## (Intercept) 2.036e+07
## AGE_CONSENT 3.898e+04
## ANT_BMI 5.309e+04
## PMRC_LAB_TRIG 8.788e+03
## PMRC_LAB_HDLCHOL 1.813e+05
## BP_SYSTOLIC_23 9.462e+04
## BP_DIASTOLIC_23 1.638e+05
## PMRC_LAB_GLU 1.616e+04
## PMRC_LAB_GH 1.016e+06
## ANT_MEAS_WAIST_CM 8.796e+04
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanobrevibacter 1.018e+09
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanosphaera 2.032e+10
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae.D_5__Methanomassiliicoccus 6.721e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Actinomyces 3.530e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Mobiluncus 1.659e+13
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Varibaculum 1.514e+11
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae.D_5__Bifidobacterium 5.128e+07
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae.D_5__Lawsonella 1.641e+13
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae.D_5__Rothia 4.878e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Libanicoccus 7.526e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Olsenella 1.948e+11
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae.D_5__Collinsella 4.363e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis.D_5__uncultured 2.203e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Adlercreutzia 1.504e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__CHKCI002 3.877e+11
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Eggerthella 7.163e+08
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Enterorhabdus 1.665e+10
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Gordonibacter 5.048e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Parvibacter 1.585e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Senegalimassilia 8.408e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Slackia 7.371e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__uncultured 6.202e+09
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured.D_5__uncultured.bacterium 7.440e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae.D_5__Bacteroides 1.047e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Barnesiella 1.739e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Coprobacter 1.195e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__uncultured 3.623e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae.D_5__Dysgonomonas 1.065e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Butyricimonas 8.951e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Odoribacter 4.852e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Sanguibacteroides 2.477e+12
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__Muribaculum NA
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__metagenome 1.250e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.Porphyromonadaceae.bacterium 1.196e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.bacterium 4.234e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.organism 2.205e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.__ 4.860e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae.D_5__Porphyromonas 1.899e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Alloprevotella 1.743e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Paraprevotella 6.825e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella 8.421e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.2 1.727e+08
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.6 4.238e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.7 7.072e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.9 6.615e+06
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.NK3B31.group 1.493e+11
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.UCG.001 2.396e+11
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__uncultured 1.071e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.__ 1.141e+13
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Alistipes 3.199e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Millionella 1.555e+13
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenella 2.387e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenellaceae.RC9.gut.group 1.301e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae.D_5__Parabacteroides 7.521e+07
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__gut.metagenome 2.538e+11
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__uncultured.bacterium 2.680e+10
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__.__ 1.166e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae.D_5__uncultured 4.277e+09
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae.D_5__Sphingobacterium NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111.D_5__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306.D_5__Clostridium.sp..CAG.306 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium.D_5__uncultured.bacterium 8.151e+08
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__.__ 8.274e+09
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae.D_5__Campylobacter 6.243e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI.D_5__Gemella 7.803e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae.D_5__Staphylococcus 9.659e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Carnobacterium 9.070e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Granulicatella 1.755e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae.D_5__Enterococcus 9.782e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Lactobacillus 1.676e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Pediococcus 6.810e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Leuconostoc 1.031e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Weissella 4.328e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Lactococcus 5.860e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Streptococcus 7.601e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Catabacter 7.519e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Christensenellaceae.R.7.group 1.514e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__uncultured 8.261e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.__ 1.194e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.1 3.159e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.13 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__Clostridiales.bacterium.enrichment.culture.clone.06.1235251.67 4.686e+12
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__gut.metagenome 5.920e+11
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__metagenome NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.Thermoanaerobacterales.bacterium 9.596e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.bacterium 2.057e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.organism 1.693e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae.D_5__Defluviitaleaceae.UCG.011 1.442e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Anaerofustis 8.806e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Eubacterium 1.265e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Anaerococcus 1.424e+12
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Ezakiella 3.487e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Finegoldia 1.400e+11
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Parvimonas 3.976e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Peptoniphilus 7.399e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__W5053 2.399e+13
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.AD3011.group 4.319e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.UCG.001 8.592e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Mogibacterium 9.722e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..brachy.group 7.943e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..nodatum.group 6.538e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__uncultured 3.410e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.__ 4.087e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Acetitomaculum 9.592e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Agathobacter 3.162e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerosporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerostipes 4.043e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Blautia 5.086e+06
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Butyrivibrio 1.423e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CAG.56 3.459e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CHKCI001 2.070e+10
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cellulosilyticum 9.462e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.1 2.976e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.2 3.698e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.3 1.244e+08
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cuneatibacter 4.267e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Dorea 7.308e+07
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Eisenbergiella 2.931e+09
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Epulopiscium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Fusicatenibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066575 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066755 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Howardella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Hungatella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.FCS020.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.ND3007.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK3A20.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4A136.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4B4.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.001 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lactonifactor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Marvinbryantia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Moryella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Murimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Oribacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Roseburia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Sellimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Shuttleworthia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.4 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__UC5.1.2E3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..eligens.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..fissicatena.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..hallii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ruminantium.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ventriosum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..xylanophilum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gauvreauii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gnavus.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..torques.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__Peptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Clostridioides NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Intestinibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Paeniclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptostreptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Romboutsia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Terrisporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Acetanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerofilum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerotruncus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Angelakisella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Butyricicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__CAG.352 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Candidatus.Soleaferrea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Caproiciproducens NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__DTU089 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Faecalibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Flavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Fournierella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__GCA.900066225 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Harryflintia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Hydrogenoanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Intestinimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Negativibacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Phocea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Pseudoflavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.5 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.6 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.9 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.NK4A214.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.002 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.005 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.007 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.009 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.013 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.014 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.2 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Subdoligranulum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__UBA1819 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__.Eubacterium..coprostanoligenes.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Candidatus.Stoquefichus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Catenibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Coprobacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Dielma NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelatoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalibaculum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalitalea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemanella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemania NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Merdibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Solobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Turicibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__.Clostridium..innocuum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Acidaminococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Phascolarctobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Succiniclasticum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Allisonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaeroglobus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaerovibrio NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Dialister NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megamonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megasphaera NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Mitsuokella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Veillonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.__.__.__.__ NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Cetobacterium NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Fusobacterium NA
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae.D_5__Victivallis NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__Azospirillum.sp..47_25 NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Bilophila NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Desulfovibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Mailhella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae.D_5__Succinivibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Comamonas NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Parasutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Sutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Escherichia.Shigella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Klebsiella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Proteus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae.D_5__Haemophilus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae.D_5__Pseudomonas NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Cloacibacillus NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Fretibacterium NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Pyramidobacter NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism.D_5__uncultured.organism NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822.D_5__Firmicutes.bacterium.CAG.822 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202.D_5__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39.D_5__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__.__ NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae.D_5__Akkermansia NA
## z value
## (Intercept) 0
## AGE_CONSENT 0
## ANT_BMI 0
## PMRC_LAB_TRIG 0
## PMRC_LAB_HDLCHOL 0
## BP_SYSTOLIC_23 0
## BP_DIASTOLIC_23 0
## PMRC_LAB_GLU 0
## PMRC_LAB_GH 0
## ANT_MEAS_WAIST_CM 0
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanobrevibacter 0
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanosphaera 0
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae.D_5__Methanomassiliicoccus 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Actinomyces 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Mobiluncus 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Varibaculum 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae.D_5__Bifidobacterium 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae.D_5__Lawsonella 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae.D_5__Rothia 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Libanicoccus 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Olsenella 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae.D_5__Collinsella 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis.D_5__uncultured 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Adlercreutzia 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__CHKCI002 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Eggerthella 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Enterorhabdus 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Gordonibacter 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Parvibacter 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Senegalimassilia 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Slackia 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__uncultured 0
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured.D_5__uncultured.bacterium 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae.D_5__Bacteroides 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Barnesiella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Coprobacter 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__uncultured 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae.D_5__Dysgonomonas 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Butyricimonas 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Odoribacter 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Sanguibacteroides 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__Muribaculum NA
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__metagenome 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.Porphyromonadaceae.bacterium 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.bacterium 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.organism 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.__ 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae.D_5__Porphyromonas 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Alloprevotella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Paraprevotella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.2 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.6 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.7 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.9 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.NK3B31.group 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.UCG.001 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__uncultured 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.__ 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Alistipes 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Millionella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenella 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenellaceae.RC9.gut.group 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae.D_5__Parabacteroides 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__gut.metagenome 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__uncultured.bacterium 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__.__ 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae.D_5__uncultured 0
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae.D_5__Sphingobacterium NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111.D_5__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306.D_5__Clostridium.sp..CAG.306 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium.D_5__uncultured.bacterium 0
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__.__ 0
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae.D_5__Campylobacter 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI.D_5__Gemella 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae.D_5__Staphylococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Carnobacterium 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Granulicatella 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae.D_5__Enterococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Lactobacillus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Pediococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Leuconostoc 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Weissella 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Lactococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Streptococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Catabacter 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Christensenellaceae.R.7.group 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__uncultured 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.__ 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.1 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.13 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__Clostridiales.bacterium.enrichment.culture.clone.06.1235251.67 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__gut.metagenome 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__metagenome NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.Thermoanaerobacterales.bacterium 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.bacterium 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.organism 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae.D_5__Defluviitaleaceae.UCG.011 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Anaerofustis 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Eubacterium 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Anaerococcus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Ezakiella 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Finegoldia 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Parvimonas 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Peptoniphilus 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__W5053 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.AD3011.group 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.UCG.001 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Mogibacterium 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..brachy.group 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..nodatum.group 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__uncultured 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.__ 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Acetitomaculum 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Agathobacter 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerosporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerostipes 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Blautia 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Butyrivibrio 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CAG.56 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CHKCI001 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cellulosilyticum 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.1 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.2 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.3 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cuneatibacter 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Dorea 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Eisenbergiella 0
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Epulopiscium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Fusicatenibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066575 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066755 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Howardella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Hungatella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.FCS020.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.ND3007.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK3A20.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4A136.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4B4.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.001 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lactonifactor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Marvinbryantia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Moryella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Murimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Oribacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Roseburia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Sellimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Shuttleworthia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.4 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__UC5.1.2E3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..eligens.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..fissicatena.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..hallii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ruminantium.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ventriosum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..xylanophilum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gauvreauii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gnavus.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..torques.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__Peptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Clostridioides NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Intestinibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Paeniclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptostreptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Romboutsia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Terrisporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Acetanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerofilum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerotruncus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Angelakisella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Butyricicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__CAG.352 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Candidatus.Soleaferrea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Caproiciproducens NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__DTU089 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Faecalibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Flavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Fournierella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__GCA.900066225 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Harryflintia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Hydrogenoanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Intestinimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Negativibacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Phocea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Pseudoflavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.5 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.6 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.9 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.NK4A214.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.002 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.005 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.007 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.009 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.013 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.014 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.2 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Subdoligranulum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__UBA1819 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__.Eubacterium..coprostanoligenes.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Candidatus.Stoquefichus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Catenibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Coprobacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Dielma NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelatoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalibaculum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalitalea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemanella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemania NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Merdibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Solobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Turicibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__.Clostridium..innocuum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Acidaminococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Phascolarctobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Succiniclasticum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Allisonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaeroglobus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaerovibrio NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Dialister NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megamonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megasphaera NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Mitsuokella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Veillonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.__.__.__.__ NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Cetobacterium NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Fusobacterium NA
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae.D_5__Victivallis NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__Azospirillum.sp..47_25 NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Bilophila NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Desulfovibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Mailhella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae.D_5__Succinivibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Comamonas NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Parasutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Sutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Escherichia.Shigella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Klebsiella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Proteus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae.D_5__Haemophilus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae.D_5__Pseudomonas NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Cloacibacillus NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Fretibacterium NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Pyramidobacter NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism.D_5__uncultured.organism NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822.D_5__Firmicutes.bacterium.CAG.822 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202.D_5__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39.D_5__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__.__ NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae.D_5__Akkermansia NA
## Pr(>|z|)
## (Intercept) 1
## AGE_CONSENT 1
## ANT_BMI 1
## PMRC_LAB_TRIG 1
## PMRC_LAB_HDLCHOL 1
## BP_SYSTOLIC_23 1
## BP_DIASTOLIC_23 1
## PMRC_LAB_GLU 1
## PMRC_LAB_GH 1
## ANT_MEAS_WAIST_CM 1
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanobrevibacter 1
## D_0__Archaea.D_1__Euryarchaeota.D_2__Methanobacteria.D_3__Methanobacteriales.D_4__Methanobacteriaceae.D_5__Methanosphaera 1
## D_0__Archaea.D_1__Euryarchaeota.D_2__Thermoplasmata.D_3__Methanomassiliicoccales.D_4__Methanomassiliicoccaceae.D_5__Methanomassiliicoccus 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Actinomyces 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Mobiluncus 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Actinomycetales.D_4__Actinomycetaceae.D_5__Varibaculum 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Bifidobacteriales.D_4__Bifidobacteriaceae.D_5__Bifidobacterium 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Corynebacteriales.D_4__Corynebacteriaceae.D_5__Lawsonella 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Actinobacteria.D_3__Micrococcales.D_4__Micrococcaceae.D_5__Rothia 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Libanicoccus 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Atopobiaceae.D_5__Olsenella 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriaceae.D_5__Collinsella 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Coriobacteriales.Incertae.Sedis.D_5__uncultured 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Adlercreutzia 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__CHKCI002 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Eggerthella 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Enterorhabdus 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Gordonibacter 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Parvibacter 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Senegalimassilia 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__Slackia 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__Eggerthellaceae.D_5__uncultured 1
## D_0__Bacteria.D_1__Actinobacteria.D_2__Coriobacteriia.D_3__Coriobacteriales.D_4__uncultured.D_5__uncultured.bacterium 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Bacteroidaceae.D_5__Bacteroides 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Barnesiella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__Coprobacter 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Barnesiellaceae.D_5__uncultured 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Dysgonomonadaceae.D_5__Dysgonomonas 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Butyricimonas 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Odoribacter 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Marinifilaceae.D_5__Sanguibacteroides 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__Muribaculum NA
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__metagenome 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.Porphyromonadaceae.bacterium 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.bacterium 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.D_5__uncultured.organism 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Muribaculaceae.__ 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Porphyromonadaceae.D_5__Porphyromonas 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Alloprevotella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Paraprevotella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.2 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.6 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.7 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotella.9 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.NK3B31.group 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__Prevotellaceae.UCG.001 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.D_5__uncultured 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Prevotellaceae.__ 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Alistipes 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Millionella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenella 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Rikenellaceae.D_5__Rikenellaceae.RC9.gut.group 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__Tannerellaceae.D_5__Parabacteroides 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__gut.metagenome 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.D_4__uncultured.D_5__uncultured.bacterium 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Bacteroidales.__.__ 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Flavobacteriales.D_4__Flavobacteriaceae.D_5__uncultured 1
## D_0__Bacteria.D_1__Bacteroidetes.D_2__Bacteroidia.D_3__Sphingobacteriales.D_4__Sphingobacteriaceae.D_5__Sphingobacterium NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Candidatus.Gastranaerophilales.bacterium.Zag_111.D_5__Candidatus.Gastranaerophilales.bacterium.Zag_111 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__Clostridium.sp..CAG.306.D_5__Clostridium.sp..CAG.306 NA
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.D_4__uncultured.bacterium.D_5__uncultured.bacterium 1
## D_0__Bacteria.D_1__Cyanobacteria.D_2__Melainabacteria.D_3__Gastranaerophilales.__.__ 1
## D_0__Bacteria.D_1__Epsilonbacteraeota.D_2__Campylobacteria.D_3__Campylobacterales.D_4__Campylobacteraceae.D_5__Campylobacter 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Family.XI.D_5__Gemella 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Bacillales.D_4__Staphylococcaceae.D_5__Staphylococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Carnobacterium 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Carnobacteriaceae.D_5__Granulicatella 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Enterococcaceae.D_5__Enterococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Lactobacillus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Lactobacillaceae.D_5__Pediococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Leuconostoc 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Leuconostocaceae.D_5__Weissella 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Lactococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Bacilli.D_3__Lactobacillales.D_4__Streptococcaceae.D_5__Streptococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Catabacter 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__Christensenellaceae.R.7.group 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.D_5__uncultured 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Christensenellaceae.__ 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.1 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiaceae.1.D_5__Clostridium.sensu.stricto.13 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__Clostridiales.bacterium.enrichment.culture.clone.06.1235251.67 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__gut.metagenome 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__metagenome NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.Thermoanaerobacterales.bacterium 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.bacterium 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Clostridiales.vadinBB60.group.D_5__uncultured.organism 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Defluviitaleaceae.D_5__Defluviitaleaceae.UCG.011 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Anaerofustis 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Eubacteriaceae.D_5__Eubacterium 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Anaerococcus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Ezakiella 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Finegoldia 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Parvimonas 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__Peptoniphilus 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XI.D_5__W5053 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.AD3011.group 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Family.XIII.UCG.001 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__Mogibacterium 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..brachy.group 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__.Eubacterium..nodatum.group 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.D_5__uncultured 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Family.XIII.__ 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Acetitomaculum 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Agathobacter 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerosporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Anaerostipes 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Blautia 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Butyrivibrio 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CAG.56 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__CHKCI001 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cellulosilyticum 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.1 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.2 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Coprococcus.3 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Cuneatibacter 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Dorea 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Eisenbergiella 1
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Epulopiscium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Fusicatenibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066575 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__GCA.900066755 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Howardella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Hungatella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.FCS020.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.ND3007.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK3A20.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4A136.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.NK4B4.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.001 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lachnospiraceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Lactonifactor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Marvinbryantia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Moryella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Murimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Oribacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Roseburia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Sellimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Shuttleworthia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__Tyzzerella.4 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__UC5.1.2E3 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..eligens.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..fissicatena.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..hallii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ruminantium.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..ventriosum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Eubacterium..xylanophilum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gauvreauii.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..gnavus.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__.Ruminococcus..torques.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Lachnospiraceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__Peptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Clostridioides NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Intestinibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Paeniclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Peptostreptococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Romboutsia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Peptostreptococcaceae.D_5__Terrisporobacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Acetanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerofilum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Anaerotruncus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Angelakisella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Butyricicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__CAG.352 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Candidatus.Soleaferrea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Caproiciproducens NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__DTU089 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Faecalibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Flavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Fournierella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__GCA.900066225 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Harryflintia NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Hydrogenoanaerobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Intestinimonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Negativibacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Oscillospira NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Phocea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Pseudoflavonifractor NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.5 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.6 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminiclostridium.9 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.NK4A214.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.002 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.005 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.007 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.008 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.009 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.010 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.013 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcaceae.UCG.014 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.1 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Ruminococcus.2 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__Subdoligranulum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__UBA1819 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__.Eubacterium..coprostanoligenes.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.D_4__Ruminococcaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.D_3__Clostridiales.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Clostridia.__.__.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Candidatus.Stoquefichus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Catenibacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Coprobacillus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Dielma NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelatoclostridium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.003 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Erysipelotrichaceae.UCG.004 NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalibaculum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalicoccus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Faecalitalea NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemanella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Holdemania NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Merdibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Solobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__Turicibacter NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__.Clostridium..innocuum.group NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Erysipelotrichia.D_3__Erysipelotrichales.D_4__Erysipelotrichaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Acidaminococcus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Phascolarctobacterium NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Acidaminococcaceae.D_5__Succiniclasticum NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Allisonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaeroglobus NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Anaerovibrio NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Dialister NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megamonas NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Megasphaera NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Mitsuokella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__Veillonella NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Firmicutes.D_2__Negativicutes.D_3__Selenomonadales.D_4__Veillonellaceae.__ NA
## D_0__Bacteria.D_1__Firmicutes.__.__.__.__ NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Cetobacterium NA
## D_0__Bacteria.D_1__Fusobacteria.D_2__Fusobacteriia.D_3__Fusobacteriales.D_4__Fusobacteriaceae.D_5__Fusobacterium NA
## D_0__Bacteria.D_1__Lentisphaerae.D_2__Lentisphaeria.D_3__Victivallales.D_4__Victivallaceae.D_5__Victivallis NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__Azospirillum.sp..47_25 NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.D_3__Rhodospirillales.D_4__uncultured.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Alphaproteobacteria.__.__.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Bilophila NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Desulfovibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__Mailhella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Deltaproteobacteria.D_3__Desulfovibrionales.D_4__Desulfovibrionaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Aeromonadales.D_4__Succinivibrionaceae.D_5__Succinivibrio NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Comamonas NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Parasutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.D_5__Sutterella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Betaproteobacteriales.D_4__Burkholderiaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Escherichia.Shigella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Klebsiella NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.D_5__Proteus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Enterobacteriales.D_4__Enterobacteriaceae.__ NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pasteurellales.D_4__Pasteurellaceae.D_5__Haemophilus NA
## D_0__Bacteria.D_1__Proteobacteria.D_2__Gammaproteobacteria.D_3__Pseudomonadales.D_4__Pseudomonadaceae.D_5__Pseudomonas NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Cloacibacillus NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Fretibacterium NA
## D_0__Bacteria.D_1__Synergistetes.D_2__Synergistia.D_3__Synergistales.D_4__Synergistaceae.D_5__Pyramidobacter NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Izimaplasmatales.D_4__uncultured.organism.D_5__uncultured.organism NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__Firmicutes.bacterium.CAG.822.D_5__Firmicutes.bacterium.CAG.822 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__gut.metagenome.D_5__gut.metagenome NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.adhufec202.D_5__uncultured.bacterium.adhufec202 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__uncultured.bacterium.D_5__uncultured.bacterium NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.D_4__unidentified.rumen.bacterium.RF39.D_5__unidentified.rumen.bacterium.RF39 NA
## D_0__Bacteria.D_1__Tenericutes.D_2__Mollicutes.D_3__Mollicutes.RF39.__.__ NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Opitutales.D_4__Puniceicoccaceae.D_5__uncultured NA
## D_0__Bacteria.D_1__Verrucomicrobia.D_2__Verrucomicrobiae.D_3__Verrucomicrobiales.D_4__Akkermansiaceae.D_5__Akkermansia NA
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1.6707e+02 on 120 degrees of freedom
## Residual deviance: 7.0199e-10 on 0 degrees of freedom
## (167 observations deleted due to missingness)
## AIC: 242
##
## Number of Fisher Scoring iterations: 25#Male
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_male <- colSums(Filter(is.numeric,merged.table_male))
zero_cols <- names(colsums_male[colsums_male==0 & !is.na(colsums_male)])
# re-define merged.tale without cols that sum to zero
merged.table_male <- merged.table_male %>% select(-one_of(zero_cols))
#Female
# the code below checks the sum of all numeric cols and removes any cols that sum to zero
colsums_female <- colSums(Filter(is.numeric,merged.table_female))
zero_cols <- names(colsums_female[colsums_female==0 & !is.na(colsums_female)])
# re-define merged.tale without cols that sum to zero
merged.table_female <- merged.table_female %>% select(-one_of(zero_cols))#Correlation test
multi_assoc <- function(mdf,exp_vars, res_vars, p.zeros=.25,adj.zero=TRUE,method, inc.zeros=TRUE, run_ratio = TRUE){
#Arguments:
# mdf - dataframe where the explanitory and response variables are as columns and subjects are rows
# exp_vars - a vector of numbers that indicate the explanitory variable cols in mdf
# res_vars - a vector of numbers that indicate the response variable cols in mdf
# p.zeros - the max amount of zeros in a singe variable that will be tolerated (expressed as a percent)
# this taxa has to be 25% present in all samples to run the analysis.
# adj.zeros - add a small value to each datapoint (0.1% of the variable average) to allow for divide by zero
# add small number, so it's close to 0 but has some values (if running correlation with 0, causes problems)
# method - 'spearman', 'pearson'
# spearman brings values high to low [ranking]; whereas pearson uses the actual values; spearman corrects/reduces variation but p value less significant.
# inc.zeros - TRUE = include all zeros in exp_vars for analysis. FALSE = replace all zeros wtih NA
# run_ration - TRUE = it will run correlations for every exp_var1:exp_var2 combination to the response variable. If not "TRUE", will only run correlations with exp_var1 vs res_var
# modification of multi_assoc(method = 'spearman', ). Only divides and multiplys explanitory variables
if (inc.zeros==FALSE){
mdf[,exp_vars] <- apply(mdf[,exp_vars], 2, function(x) ifelse(x == 0, NA, x))
}
mdf$None <- c(rep(1,nrow(mdf)))
exp_vars <- c(exp_vars,match('None', colnames(mdf)))
d <- list()
n <- 0
t=1
col_headers <- c("Explanitory_var1","Explanitory_var2","Response_var", "Estimate", "c.Pval", "Rsq", 'r.Pval')
if(adj.zero == TRUE){ #check to see if adjustment was specified
adj=1
}else{
adj=0
}
for (i in exp_vars[-length(exp_vars)]) { #for every exp variable. This excludes the "None" column added in line above
if ((length(which(mdf[,i]==0|is.na(mdf[,i])==TRUE))<p.zeros*length(mdf[,i])) & (is.numeric(mdf[,i])==TRUE)){ # if the percent of zeros in exp var is less than the specified p.zero threshold, continue, else skip exp var
if (run_ratio == TRUE){
list2 <- exp_vars[-match(i, exp_vars)] #makes a list of all variables except for "i" variable to loop over (for divide only, see multiply below)
} else {
list2 <- 'None'
}
for (j in list2){
if ((length(which(mdf[,j]==0|is.na(mdf[,j])==TRUE))<p.zeros*length(mdf[,j])) & (is.numeric(mdf[,j])==TRUE)){ #check to see if comparison exp var meets p.zero threshold
for (x in res_vars){ #loops over every response variable
#run tests
z <- (mdf[,i]+adj*(0.001*mean(mdf[,i],na.rm = T)))/(mdf[,j]+adj*(0.001*mean(mdf[,j],na.rm = T)))
z[is.infinite(z)] <- NA
y <- mdf[,x]
tmp <- cor.test(y, z, method = method)
reg <- summary(lm(y~z))
tmp_df <- data.frame(
colnames(mdf[i]),
colnames(mdf[j]),
colnames(mdf[x]),
as.numeric(tmp[4]),
as.numeric(tmp[3]),
as.numeric(reg$r.squared),
as.numeric(try(pf(reg$fstatistic[1],reg$fstatistic[2],reg$fstatistic[3], lower.tail = F),silent = T)),
stringsAsFactors = FALSE)
n <- n+1
d[[n]] <- tmp_df
}
}
}
}
if (t > (length(exp_vars)/100)){ # prints status as a percentage
print(paste(match(i,exp_vars)/length(exp_vars)*100,"%"))
t=1
}else{
t=t+1
}
}
tmp_results <- do.call(rbind,d)
colnames(tmp_results) <- col_headers
tmp_results <- as_data_frame(tmp_results)
tmp_results$c.fdr <- p.adjust(tmp_results$c.Pval)
cp <- match("c.Pval", colnames(tmp_results))
tmp_results <- tmp_results[,c(1:cp, ncol(tmp_results),(cp+1):(ncol(tmp_results)-1))]
tmp_results$r.fdr <- p.adjust(tmp_results$r.Pval)
multi_corr_tbl <- tmp_results[order(tmp_results$c.Pval, decreasing = F),]
rownames(multi_corr_tbl) <- c(1:nrow(multi_corr_tbl))
multi_corr_tbl$index <- c(1:nrow(multi_corr_tbl))
if (run_ratio != TRUE){
multi_corr_tbl <- multi_corr_tbl[,-2]
}
return(multi_corr_tbl)
}
#####################################
#####################################
plot_comprsn <- function (data_tbl,comp_tbl,row,color.by='All samples'){
#Arguments:
# data_tbl - dataframe where the explanitory and response variables are as columns and subjects are rows (same df used in multi_pred_cor())
# comp_tbl - comparison table, this is the output of multi_pred_cor()
# row - the row of the comp_tbl you want to plot (row 1 is the most significant correlation)
# color.by - the col of data_table to use as input for aes(color =). Must specify the col with $.
if (colnames(comp_tbl[2]) != "Explanitory_var2"){
tops <- c(comp_tbl[[row,1]], "", comp_tbl[[row,2]])
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
tops <- c(comp_tbl[[row,1]], comp_tbl[[row,2]], comp_tbl[[row,3]])
if (tops[2]=="None"){
z=data_tbl[[tops[1]]]
label <- tops[1]
} else {
z <- do.call('/',list(data_tbl[[tops[1]]],data_tbl[[tops[2]]]))
z[is.infinite(z)] <- NA
label <- paste(tops[1], tops[2], sep = paste("_##","/","##_"))
}
}
y <- tops[3]
data_tbl$zn <- z
x <- 'zn'
my.formula <- x~y
if (color.by != "All samples"){
color.by <- data_tbl[,color.by]
colors <- scale_color_brewer(palette="Set1")
} else {
colors <- scale_color_manual(values = 'black')
}
plot <- data_tbl %>% ggplot(aes(x = data_tbl$zn, y = data_tbl[,y], color=color.by))+
xlab(label)+
ylab(tops[3])+
geom_point()+
colors+
stat_cor(method = 'spearman', label.y.npc = .95)+
geom_smooth(method=lm, se=F)
return(plot)
}#Scaterplot and heatmap ##BMI
#Try each phenotype of the disease: phe = c(6:12,14,15)
phe = c(5) #BMI
#phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"
## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 2 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
##TRIG
phe = c(6) #TRIG
# phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 73 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 73 rows containing non-finite values (stat_smooth).## Warning: Removed 73 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 88 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 88 rows containing non-finite values (stat_smooth).## Warning: Removed 88 rows containing missing values (geom_point).
##HDL
phe = c(7) #HDL
# phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##Systolic
phe = c(8) #Systolic
# phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##Diastolic
phe = c(9) #Diastolic
# phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 1 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
##GLU
phe = c(10) #GLU
# phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##GH
phe = c(11) #GH
# phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 74 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 74 rows containing non-finite values (stat_smooth).## Warning: Removed 74 rows containing missing values (geom_point).
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Removed 90 rows containing non-finite values (stat_cor).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 90 rows containing non-finite values (stat_smooth).## Warning: Removed 90 rows containing missing values (geom_point).
##AGE
phe = c(13) #AGE
# phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
##Waist
phe = c(14) #Waist
taxa = c(20:285) #from merged.table_male
cor_table = multi_assoc(merged.table_male, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.12359550561798 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "2.24719101123596 %"
## [1] "3.37078651685393 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.49438202247191 %"
## [1] "5.61797752808989 %"
## [1] "6.74157303370786 %"
## [1] "7.86516853932584 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.98876404494382 %"
## [1] "10.1123595505618 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "11.2359550561798 %"
## [1] "12.3595505617978 %"
## [1] "13.4831460674157 %"
## [1] "14.6067415730337 %"
## [1] "15.7303370786517 %"
## [1] "16.8539325842697 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "17.9775280898876 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.1011235955056 %"
## [1] "20.2247191011236 %"
## [1] "21.3483146067416 %"
## [1] "22.4719101123595 %"
## [1] "23.5955056179775 %"
## [1] "24.7191011235955 %"
## [1] "25.8426966292135 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "26.9662921348315 %"
## [1] "28.0898876404494 %"
## [1] "29.2134831460674 %"
## [1] "30.3370786516854 %"
## [1] "31.4606741573034 %"
## [1] "32.5842696629214 %"
## [1] "33.7078651685393 %"
## [1] "34.8314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "35.9550561797753 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.0786516853933 %"
## [1] "38.2022471910112 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "39.3258426966292 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "40.4494382022472 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.5730337078652 %"
## [1] "42.6966292134831 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.8202247191011 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "44.9438202247191 %"
## [1] "46.0674157303371 %"
## [1] "47.1910112359551 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "48.314606741573 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.438202247191 %"
## [1] "50.561797752809 %"
## [1] "51.685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "52.8089887640449 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.9325842696629 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "55.0561797752809 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.1797752808989 %"
## [1] "57.3033707865169 %"
## [1] "58.4269662921348 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "59.5505617977528 %"
## [1] "60.6741573033708 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.7977528089888 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.9213483146067 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.0449438202247 %"
## [1] "65.1685393258427 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "66.2921348314607 %"
## [1] "67.4157303370787 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "68.5393258426966 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.6629213483146 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.7865168539326 %"
## [1] "71.9101123595506 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.0337078651685 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.1573033707865 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.2808988764045 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.4044943820225 %"
## [1] "77.5280898876404 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "78.6516853932584 %"
## [1] "79.7752808988764 %"
## [1] "80.8988764044944 %"
## [1] "82.0224719101124 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "83.1460674157303 %"
## [1] "84.2696629213483 %"
## [1] "85.3932584269663 %"
## [1] "86.5168539325843 %"
## [1] "87.6404494382023 %"
## [1] "88.7640449438202 %"
## [1] "89.8876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.0112359550562 %"
## [1] "92.1348314606742 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.2584269662921 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "94.3820224719101 %"
## [1] "95.5056179775281 %"
## [1] "96.6292134831461 %"
## [1] "97.752808988764 %"
## [1] "98.876404494382 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_male, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
taxa = c(20:291) #from merged.table_female
cor_table = multi_assoc(merged.table_female, taxa, phe, method = "spearman", run_ratio = FALSE, p.zeros = 0.5)## [1] "1.0989010989011 %"
## [1] "2.1978021978022 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "3.2967032967033 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "4.3956043956044 %"
## [1] "5.49450549450549 %"
## [1] "6.59340659340659 %"
## [1] "7.69230769230769 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "8.79120879120879 %"
## [1] "9.89010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "10.989010989011 %"
## [1] "12.0879120879121 %"
## [1] "13.1868131868132 %"
## [1] "14.2857142857143 %"
## [1] "15.3846153846154 %"
## [1] "16.4835164835165 %"
## [1] "17.5824175824176 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "18.6813186813187 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "19.7802197802198 %"
## [1] "20.8791208791209 %"
## [1] "21.978021978022 %"
## [1] "23.0769230769231 %"
## [1] "24.1758241758242 %"
## [1] "25.2747252747253 %"
## [1] "26.3736263736264 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "27.4725274725275 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "28.5714285714286 %"
## [1] "29.6703296703297 %"
## [1] "30.7692307692308 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "31.8681318681319 %"
## [1] "32.967032967033 %"
## [1] "34.0659340659341 %"
## [1] "35.1648351648352 %"
## [1] "36.2637362637363 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "37.3626373626374 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "38.4615384615385 %"
## [1] "39.5604395604396 %"
## [1] "40.6593406593407 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "41.7582417582418 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "42.8571428571429 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "43.956043956044 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "45.0549450549451 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "46.1538461538462 %"
## [1] "47.2527472527472 %"
## [1] "48.3516483516484 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "49.4505494505495 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "50.5494505494505 %"
## [1] "51.6483516483517 %"
## [1] "52.7472527472528 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "53.8461538461538 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "54.9450549450549 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "56.043956043956 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "57.1428571428571 %"
## [1] "58.2417582417582 %"
## [1] "59.3406593406593 %"
## [1] "60.4395604395604 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "61.5384615384615 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "62.6373626373626 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "63.7362637362637 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "64.8351648351648 %"
## [1] "65.9340659340659 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "67.032967032967 %"
## [1] "68.1318681318681 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "69.2307692307692 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "70.3296703296703 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "71.4285714285714 %"
## [1] "72.5274725274725 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "73.6263736263736 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "74.7252747252747 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "75.8241758241758 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "76.9230769230769 %"
## [1] "78.021978021978 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties
## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "79.1208791208791 %"
## [1] "80.2197802197802 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "81.3186813186813 %"
## [1] "82.4175824175824 %"
## [1] "83.5164835164835 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "84.6153846153846 %"
## [1] "85.7142857142857 %"
## [1] "86.8131868131868 %"
## [1] "87.9120879120879 %"
## [1] "89.010989010989 %"
## [1] "90.1098901098901 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "91.2087912087912 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "92.3076923076923 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## [1] "93.4065934065934 %"
## [1] "94.5054945054945 %"
## [1] "95.6043956043956 %"
## [1] "96.7032967032967 %"
## [1] "97.8021978021978 %"
## [1] "98.9010989010989 %"## Warning in cor.test.default(y, z, method = method): Cannot compute exact p-value
## with ties## Warning: Setting row names on a tibble is deprecated.plot = plot_comprsn(merged.table_female, cor_table, row = 1)
plot## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.
## Warning: Use of `data_tbl$zn` is discouraged. Use `zn` instead.## `geom_smooth()` using formula 'y ~ x'
#Heatmap
#Male
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_male[,c(5:11,13,14,20:285)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_male[,c(5:11,13,14,20:285)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_male[, c(5:11, 13, 14, 20:285)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr## Warning in corrplot(Correlation, method = "ellipse", tl.col = "black", type =
## "upper", : Not been able to calculate text margin, please try again with a clean
## new empty window using {plot.new(); dev.off()} or reduce tl.cex
#Female
# Visualizing the correlation in heatmap [30x30]; try with class x class- level 3
library(corrplot)
res = cor.mtest(merged.table_female[,c(5:11,13,14,20:291)], conf.level = 0.95, rm.na = T)## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zero
## Warning in cor(x, y): the standard deviation is zeroCorrelation <- round(cor(merged.table_female[,c(5:11,13,14,20:291)],method = "spearman", use="complete.obs"), 2)## Warning in cor(merged.table_female[, c(5:11, 13, 14, 20:291)], method =
## "spearman", : the standard deviation is zerocorrplot(Correlation, method = "ellipse", tl.col = "black", type = 'upper', tl.cex = 0.4, cl.cex = 0.4, p.mat = res$p, insig = "blank", sig.level = 0.05) #heatmap; blue=neg corr; red=pos corr## Warning in corrplot(Correlation, method = "ellipse", tl.col = "black", type =
## "upper", : Not been able to calculate text margin, please try again with a clean
## new empty window using {plot.new(); dev.off()} or reduce tl.cex